Method of and system for automated machine-assisted detection of ocular disease … — Corneacare, Inc. (US12697024B2)

ABSTRACT

Abstract

Digital imaging processing system and methods for automatically recognizing ocular diseases including dry eye disease, and other ocular conditions in the eyes of humans by automatically (i) processing 2D in vivo digital images of the human eyes formed, captured and detected using a visible-wavelength operating digital camera system, and (ii) using machine-vision image processing subsystems (e.g. engines) to automatically detect the presence and location of specific objects in the ocular surfaces in the human eye, and confirm that specific conditions have been satisfied during image processing to support automated recognition of a specific ocular disease and other ocular conditions indicative of ocular pathology.

Description

BACKGROUND OF INVENTION

Field of Invention

The present invention is related to new and improved methods, systems and technologies for forming, capturing, and processing digital images of human eyes in patients in flexible and distributed ways so as to support the automated detection, recognition and/or prediction of the existence of conditions indicative of particular diseases, such as dry eye disease, and prescribed treatment and care to restore and/or preserve human vision.

Brief Description of the State of Art

Vision is an essential capacity to human life and survival, only second to vital functions such as respiration and consciousness. And yet hundreds of millions of individuals around the world suffer daily from dry eye disease (DED) which is now identified as one of the most common ocular disorders worldwide. To better understand the nature of this complex disease, it is helpful to have a basic understanding of the anatomy and physiology of the human eye.

In the human eye, the anterior segment of the eye is defined as the front third of the eye that includes the conjunctiva, cornea, iris, pupil, and the lens. The conjunctiva, iris, pupil, cornea, retina, lens, vitreous, optic nerve, choroid and sclera of the human eye are illustrated in FIG. 1 A . The epithelium, Bowman's membrane, stroma, Descemet's membrane, and endothelium of the human cornea are illustrated in FIG. 1 B . These structures constitute the path of light through the eye and the ocular refractive system. The primary function is to form clear and converging images on the retina in the back of the eye, which generate a pattern of impulses that are transmitted along the optic nerve to the visual cortex in the human brain where human perception and vision is realized.

FIG. 2 shows the lacrimal sac, canaliculi, the puncta, the tear duct (i.e. nasolacrimal duct), and the tear gland (i.e. lacrimal gland) above the eye. FIG. 3 shows the lacrimal gland, meibomian glands, and the goblet cells in the conjunctiva, with an expanded view of the tear film showing the lipid layer supplied by the meibomian glands, an aqueous layer supplied by the lacrimal glands, and the mucin layer provided by the epithelial cells. These structures produce the components of the tear film membrane, namely: aqueous saline liquid layer closest to the ocular surfaces; and lipids and oils that mix with the saline liquid to produce an outer film that inhibits evaporation of aqueous layer from the corneal surface. Any disturbance within the tear film is likely to contribute to what is called dry eye disease (DED) condition. For example, a deficiency in aqueous layer production will lead to inadequate lubrication of the corneal surface, whereas a deficient or a dysfunctional lipid layer will lead to rapid evaporation of the tear film from the corneal surface.

FIG. 4 lists some common symptoms of dry eye disease (DED), including, redness, stinging or burning, blurred vision, sensitivity to light, excessive tearing, foreign body sensation, itchiness, and eyelid asymmetry. Based on abnormalities of the tear film composition, dry eye disease (DED) has been broadly classified into two categories: (i) aqueous-deficient dry eye (ADDE); and (ii) evaporative dry eye (EDE), corresponding to disorders of the lacrimal and meibomian glands, respectively.

(i) ADDE can be classified into: (i) Sjögren's syndrome (SS), and (ii) non-Sjögren's syndrome (NSS) dry eye. (ii) EDE can be classified into intrinsic and extrinsic causes, with meibomian gland dysfunction (MGD) being the most common cause of EDE.

As ADDE and EDE are not mutually exclusive and may act together to produce signs and symptoms, it may be challenging to identify pure forms of each disease due to mixed forms of DED using the current system of classification. In addition, the initial underlying dry eye mechanism may lead to involvement of additional mechanisms, thus resulting in misclassification of the initial etiology at different stages of disease. Thus, with time and progression of disease severity, it is possible that the clinical phenotype of DED may be altered as well, which can lead to mixed clinical phenotypes.

Currently, dry eye disease severity is graded into four categories based on a combination of patient symptoms and clinical signs. However, several studies have demonstrated the lack of correlation between the severity of symptoms experienced by the patient and physical findings on clinical examination. For example, patients in early stages of DED may demonstrate symptoms, without clear clinical signs. Conversely, patients may show mild or severe objective clinical signs of DED, but may be symptom free. The disparity between signs and symptoms is multifactorial but may be explained in part by the reduction of corneal nerve density with dry eye disease. Reduced or loss of corneal sensation, secondary to reduced corneal sub-basal nerve density from nerve damage in DED, may explain the presence of clinical signs of ocular surface damage in the absence of concordant symptoms of ocular discomfort. To establish a definite diagnosis of anterior segment eye/ocular diseases, conventional practice involves evaluating both anatomy and function, typically using specialized instruments such as: slit-lamp bio-microscopy, optical coherence tomography, corneal topography, tonometry, perimetry, etc. For such historical reasons, when developing computer assisted or artificial intelligence (AI) methods of detecting such ocular diseases, various forms of data besides slit-lamp images, AS-OCT images, tear film lipid layer interference images (e.g. image), including videos, formatted parameters and texts, have been used for such purposes.

In the paper titled “IMAGE-GUIDED EVALUATION AND MONITORING OF TREATMENT RESPONSE IN PATIENTS WITH DRY EYE DISEASE (2014)” by Yureeda Qazi, et al, working in the Cornea and Ocular Surface Imaging Center, in the Department of Ophthalmology—Cornea Service, at the Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, acknowledged that the pathophysiological mechanisms involved in the development of DED are not well understood, and thus treating DED has been a significant challenge for ophthalmologists and optometrist. Most of the currently available diagnostic tests demonstrate low correlation to patient symptoms and have low reproducibility. Conventional methods propose using non-invasive in vivo imaging modalities during patient care, namely, in vivo confocal microscopy (IVCM) and optical coherence tomography (OCT), which allow real-time visualization of cellular and anatomical structures of the cornea and ocular surface. By providing both qualitative and quantitative assessment, these techniques can be used to demonstrate early subclinical disease, grade layer-by-layer severity, and allow monitoring of disease severity by cellular alterations. Imaging-guided stratification of patients may also be possible in conjunction with clinical examination methods. Visualization of subclinical changes and stratification of patients in vivo, allows objective image-guided evaluation of tailored treatment response based on cellular morphological alterations specific to each patient. This image-guided approach to DED may ultimately improve patient outcomes and allow studying the efficacy of novel therapies in clinical trials.

FIG. 5 lists primary prior art imaging modalities that are currently being used to image features in the anterior segment of the human eye during particular AI-supported diagnostic applications, and are relevant to the Background of the Present Invention. As shown in FIG. 5 , conventional imaging modalities include: (i) slit-lamp microscopes where slit light 1 A is used to produce slit lamp images 1 B during cataract detection and grading, where diffuse light 1 C is used during conjunctival hyperemia grading, high intraocular pressure detection, and identification, localization of conjunctiva and cornea and lens diseases, and where retro-illumination 1 D is used during posterior capsule opacification progress prediction; (ii) optical coherence tomography (OCT) for producing AS-OCT images 1 E for angle closure detection; (iii) tear film lipid layer interference images 1 F for tear film lipid layer classification dry eye diagnosis (DED); (iv) frontal eye videos 1 G for use during fluorescein instillation, during dry eye detection, with tear break up time (TBUT) calculation; (v) retinal fundus images 1 H for use during cataract grading and detecting gross macular or optic nerve abnormalities; and (vi) visible wavelength images 1 I captured by a digital camera for use during corneal arcus and a cataract detection. Notably, visible wavelength images are modes of imaging of great interest in view of the fact that there are billions of mobile smartphone camera systems currently in use around the world.

FIG. 6 illustrates a typical workflow and system environment 2 used when constructing models for use in deep learning (AI) systems and applications. As illustrated during the data preparation stage 3 , models are developed to train deep learning systems 5 using both traditional learning and more modern deep learning techniques 5 , as well as combinations of the same, to detect and recognize objects, and then evaluate system performance using various techniques 6 well known in the art.

In recent times, a number of researchers have suggested using the digital camera subsystem integrated in mobile smartphone systems to capture visible-wavelength images ( 1 A) for use in detecting dry eye disease (DED) conditions in the human eyes. However, most prior art proposals have required the addition of specialized optical and/or illumination apparatus outfitted to the smartphone camera to detect specific features with specialized imaging modalities, other than visible wavelength imaging. Most proposals have been aspirational in nature, identifying the use of mobile smartphone camera devices in dry eye disease (DED) detection and treatment as an industry aim, without disclosing, teaching or offering any adequate means for, way of, or enabling knowledge on how to actually and reliably realize such an important objective in the global medical industry.

Rather, the prior art has identified that there are many significant limitations and hurdles to be overcome for widespread clinical implementation of AI-assisted applications in anterior segment ocular diseases.

At the same time, the prior art has clearly recognized the need for a number of things, namely: (i) algorithms that can learn with less image data and achieve quality assessment during system training; (ii) integrative analysis of multimodal data (e.g. videos, images, formatting parameters, texts, etc.) to diagnose complicated ocular diseases (e.g. dry eye disease) which currently remains an unsolved challenge in the field; (iii) methods that will allow smartphone photography to serve as a diagnostic tool for anterior segment ocular diseases in the near future; (iv) AI systems that that can be used in clinical settings and employing systemic AI models trained on multimodal and heterogeneous data using less data-intensive algorithms; and (v) home-based systems that use AI methods for diagnosis and management of ocular diseases and related eye care problems.

Accordingly, there is a great need in the art for new and improved ways of and means for (i) capturing and processing digital images of human eyes using conventional visible-wavelength mobile smartphone camera systems, and (ii) automatically recognizing specific ocular disease conditions such as dry eye disease (DED) and other ocular pathologies, and prescribing treatment thereof and managing the ocular care needs of human beings and other animals, in both remote/distributed telemedicine and clinical care environments alike.

OBJECTS AND SUMMARY OF THE PRESENT INVENTION

Accordingly, it is a primary object of the present invention to provide a new and improved digital imaging processing system and method to automatically recognize ocular disease (OD) conditions in humans by (i) processing 2D digital images of the human eyes in real-time formed, captured and detected using a visible-wavelength operating mobile smartphone camera system, (ii) detecting the presence and location of specific objects in the ocular surfaces in the human eye, and (iii) confirming that specific conditions have been satisfied during image processing to support automated ocular disease condition recognition.

Another object of the present invention is to provide new and improved machine-learning based systems and methods to support and enable the reliable computer-aided diagnosis and treatment of ophthalmic/ocular diseases and disorders afflicting millions of human beings around the world.

Another object of the present invention is to provide such a new and improved system which uses mobile smartphone cameras and machine-vision systems, image processing and deep learning processes.

Another object of the present invention is to provide new and improved automated machine-vision processing system that automatically identifies, localizes, and extracts specific pixel content of ocular objects represented in digital images of human eyes formed and captured using visible-wavelength operating smartphone camera systems, to support automated recognition of various ocular disease (OD) conditions, including dry eye disease (DED), and prescription of subclinical treatment thereof, and thereafter monitor a prescribed course's treatment response, and continue with and/or adapt the prescribed course of treatment with minimal or no human input required, while enhancing the preclinical and/or clinical management of ocular disease conditions around the world.

Another object of the present invention is to provide new and improved automated eye-care diagnostics, treatment & management applications using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide new and improved automated computer-based automated eye-care diagnostic, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated automotive-based eye-care diagnostics, treatment & management applications using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide new and improved automated video-gaming-based automated eye-care diagnostics, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated aircraft-based automated eye-care diagnostic, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated telemedicine eye care system using visible-wavelength digital camera systems.

Another object of the present invention is to provide a novel system network in which a mobile ophthalmic image capturing and processing system is deployed and adapted for automated machine-assisted recognition of ocular disease (OD) conditions, and other anterior eye segment diseases, including automated prescription of disease treatment, and managed assessment of treatment response care.

Another object of the present invention is to provide a novel system network in which a mobile ophthalmic image capturing and processing system is deployed and adapted for automated machine-assisted recognition of dry eye disease and other anterior eye segment diseases, prescription of treatment, and assessment of treatment response, comprising (i) smartphone mobile image capturing systems for use by patients and users whose eyes are to be monitored and cared for using the system, (ii) clinical ophthalmic decision workstations, and real-time ophthalmic image driven ocular disease (DOD) condition recognition engines realized and supported within data centers deployed around the world, (iii) local environmental monitoring stations and third-party data provider platforms, (iv) a global navigation satellite system (GNSS) comprising a constellation of GNSS satellites orbiting around the earth for GP

BACKGROUND OF INVENTION

Field of Invention

The present invention is related to new and improved methods, systems and technologies for forming, capturing, and processing digital images of human eyes in patients in flexible and distributed ways so as to support the automated detection, recognition and/or prediction of the existence of conditions indicative of particular diseases, such as dry eye disease, and prescribed treatment and care to restore and/or preserve human vision.

Brief Description of the State of Art

Vision is an essential capacity to human life and survival, only second to vital functions such as respiration and consciousness. And yet hundreds of millions of individuals around the world suffer daily from dry eye disease (DED) which is now identified as one of the most common ocular disorders worldwide. To better understand the nature of this complex disease, it is helpful to have a basic understanding of the anatomy and physiology of the human eye.

In the human eye, the anterior segment of the eye is defined as the front third of the eye that includes the conjunctiva, cornea, iris, pupil, and the lens. The conjunctiva, iris, pupil, cornea, retina, lens, vitreous, optic nerve, choroid and sclera of the human eye are illustrated in FIG. 1 A . The epithelium, Bowman's membrane, stroma, Descemet's membrane, and endothelium of the human cornea are illustrated in FIG. 1 B . These structures constitute the path of light through the eye and the ocular refractive system. The primary function is to form clear and converging images on the retina in the back of the eye, which generate a pattern of impulses that are transmitted along the optic nerve to the visual cortex in the human brain where human perception and vision is realized.

FIG. 2 shows the lacrimal sac, canaliculi, the puncta, the tear duct (i.e. nasolacrimal duct), and the tear gland (i.e. lacrimal gland) above the eye. FIG. 3 shows the lacrimal gland, meibomian glands, and the goblet cells in the conjunctiva, with an expanded view of the tear film showing the lipid layer supplied by the meibomian glands, an aqueous layer supplied by the lacrimal glands, and the mucin layer provided by the epithelial cells. These structures produce the components of the tear film membrane, namely: aqueous saline liquid layer closest to the ocular surfaces; and lipids and oils that mix with the saline liquid to produce an outer film that inhibits evaporation of aqueous layer from the corneal surface. Any disturbance within the tear film is likely to contribute to what is called dry eye disease (DED) condition. For example, a deficiency in aqueous layer production will lead to inadequate lubrication of the corneal surface, whereas a deficient or a dysfunctional lipid layer will lead to rapid evaporation of the tear film from the corneal surface.

FIG. 4 lists some common symptoms of dry eye disease (DED), including, redness, stinging or burning, blurred vision, sensitivity to light, excessive tearing, foreign body sensation, itchiness, and eyelid asymmetry. Based on abnormalities of the tear film composition, dry eye disease (DED) has been broadly classified into two categories: (i) aqueous-deficient dry eye (ADDE); and (ii) evaporative dry eye (EDE), corresponding to disorders of the lacrimal and meibomian glands, respectively.

(i) ADDE can be classified into: (i) Sjögren's syndrome (SS), and (ii) non-Sjögren's syndrome (NSS) dry eye. (ii) EDE can be classified into intrinsic and extrinsic causes, with meibomian gland dysfunction (MGD) being the most common cause of EDE.

As ADDE and EDE are not mutually exclusive and may act together to produce signs and symptoms, it may be challenging to identify pure forms of each disease due to mixed forms of DED using the current system of classification. In addition, the initial underlying dry eye mechanism may lead to involvement of additional mechanisms, thus resulting in misclassification of the initial etiology at different stages of disease. Thus, with time and progression of disease severity, it is possible that the clinical phenotype of DED may be altered as well, which can lead to mixed clinical phenotypes.

Currently, dry eye disease severity is graded into four categories based on a combination of patient symptoms and clinical signs. However, several studies have demonstrated the lack of correlation between the severity of symptoms experienced by the patient and physical findings on clinical examination. For example, patients in early stages of DED may demonstrate symptoms, without clear clinical signs. Conversely, patients may show mild or severe objective clinical signs of DED, but may be symptom free. The disparity between signs and symptoms is multifactorial but may be explained in part by the reduction of corneal nerve density with dry eye disease. Reduced or loss of corneal sensation, secondary to reduced corneal sub-basal nerve density from nerve damage in DED, may explain the presence of clinical signs of ocular surface damage in the absence of concordant symptoms of ocular discomfort. To establish a definite diagnosis of anterior segment eye/ocular diseases, conventional practice involves evaluating both anatomy and function, typically using specialized instruments such as: slit-lamp bio-microscopy, optical coherence tomography, corneal topography, tonometry, perimetry, etc. For such historical reasons, when developing computer assisted or artificial intelligence (AI) methods of detecting such ocular diseases, various forms of data besides slit-lamp images, AS-OCT images, tear film lipid layer interference images (e.g. image), including videos, formatted parameters and texts, have been used for such purposes.

In the paper titled “IMAGE-GUIDED EVALUATION AND MONITORING OF TREATMENT RESPONSE IN PATIENTS WITH DRY EYE DISEASE (2014)” by Yureeda Qazi, et al, working in the Cornea and Ocular Surface Imaging Center, in the Department of Ophthalmology—Cornea Service, at the Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, acknowledged that the pathophysiological mechanisms involved in the development of DED are not well understood, and thus treating DED has been a significant challenge for ophthalmologists and optometrist. Most of the currently available diagnostic tests demonstrate low correlation to patient symptoms and have low reproducibility. Conventional methods propose using non-invasive in vivo imaging modalities during patient care, namely, in vivo confocal microscopy (IVCM) and optical coherence tomography (OCT), which allow real-time visualization of cellular and anatomical structures of the cornea and ocular surface. By providing both qualitative and quantitative assessment, these techniques can be used to demonstrate early subclinical disease, grade layer-by-layer severity, and allow monitoring of disease severity by cellular alterations. Imaging-guided stratification of patients may also be possible in conjunction with clinical examination methods. Visualization of subclinical changes and stratification of patients in vivo, allows objective image-guided evaluation of tailored treatment response based on cellular morphological alterations specific to each patient. This image-guided approach to DED may ultimately improve patient outcomes and allow studying the efficacy of novel therapies in clinical trials.

FIG. 5 lists primary prior art imaging modalities that are currently being used to image features in the anterior segment of the human eye during particular AI-supported diagnostic applications, and are relevant to the Background of the Present Invention. As shown in FIG. 5 , conventional imaging modalities include: (i) slit-lamp microscopes where slit light 1 A is used to produce slit lamp images 1 B during cataract detection and grading, where diffuse light 1 C is used during conjunctival hyperemia grading, high intraocular pressure detection, and identification, localization of conjunctiva and cornea and lens diseases, and where retro-illumination 1 D is used during posterior capsule opacification progress prediction; (ii) optical coherence tomography (OCT) for producing AS-OCT images 1 E for angle closure detection; (iii) tear film lipid layer interference images 1 F for tear film lipid layer classification dry eye diagnosis (DED); (iv) frontal eye videos 1 G for use during fluorescein instillation, during dry eye detection, with tear break up time (TBUT) calculation; (v) retinal fundus images 1 H for use during cataract grading and detecting gross macular or optic nerve abnormalities; and (vi) visible wavelength images 1 I captured by a digital camera for use during corneal arcus and a cataract detection. Notably, visible wavelength images are modes of imaging of great interest in view of the fact that there are billions of mobile smartphone camera systems currently in use around the world.

FIG. 6 illustrates a typical workflow and system environment 2 used when constructing models for use in deep learning (AI) systems and applications. As illustrated during the data preparation stage 3 , models are developed to train deep learning systems 5 using both traditional learning and more modern deep learning techniques 5 , as well as combinations of the same, to detect and recognize objects, and then evaluate system performance using various techniques 6 well known in the art.

In recent times, a number of researchers have suggested using the digital camera subsystem integrated in mobile smartphone systems to capture visible-wavelength images ( 1 A) for use in detecting dry eye disease (DED) conditions in the human eyes. However, most prior art proposals have required the addition of specialized optical and/or illumination apparatus outfitted to the smartphone camera to detect specific features with specialized imaging modalities, other than visible wavelength imaging. Most proposals have been aspirational in nature, identifying the use of mobile smartphone camera devices in dry eye disease (DED) detection and treatment as an industry aim, without disclosing, teaching or offering any adequate means for, way of, or enabling knowledge on how to actually and reliably realize such an important objective in the global medical industry.

Rather, the prior art has identified that there are many significant limitations and hurdles to be overcome for widespread clinical implementation of AI-assisted applications in anterior segment ocular diseases.

At the same time, the prior art has clearly recognized the need for a number of things, namely: (i) algorithms that can learn with less image data and achieve quality assessment during system training; (ii) integrative analysis of multimodal data (e.g. videos, images, formatting parameters, texts, etc.) to diagnose complicated ocular diseases (e.g. dry eye disease) which currently remains an unsolved challenge in the field; (iii) methods that will allow smartphone photography to serve as a diagnostic tool for anterior segment ocular diseases in the near future; (iv) AI systems that that can be used in clinical settings and employing systemic AI models trained on multimodal and heterogeneous data using less data-intensive algorithms; and (v) home-based systems that use AI methods for diagnosis and management of ocular diseases and related eye care problems.

Accordingly, there is a great need in the art for new and improved ways of and means for (i) capturing and processing digital images of human eyes using conventional visible-wavelength mobile smartphone camera systems, and (ii) automatically recognizing specific ocular disease conditions such as dry eye disease (DED) and other ocular pathologies, and prescribing treatment thereof and managing the ocular care needs of human beings and other animals, in both remote/distributed telemedicine and clinical care environments alike.

OBJECTS AND SUMMARY OF THE PRESENT INVENTION

Accordingly, it is a primary object of the present invention to provide a new and improved digital imaging processing system and method to automatically recognize ocular disease (OD) conditions in humans by (i) processing 2D digital images of the human eyes in real-time formed, captured and detected using a visible-wavelength operating mobile smartphone camera system, (ii) detecting the presence and location of specific objects in the ocular surfaces in the human eye, and (iii) confirming that specific conditions have been satisfied during image processing to support automated ocular disease condition recognition.

Another object of the present invention is to provide new and improved machine-learning based systems and methods to support and enable the reliable computer-aided diagnosis and treatment of ophthalmic/ocular diseases and disorders afflicting millions of human beings around the world.

Another object of the present invention is to provide such a new and improved system which uses mobile smartphone cameras and machine-vision systems, image processing and deep learning processes.

Another object of the present invention is to provide new and improved automated machine-vision processing system that automatically identifies, localizes, and extracts specific pixel content of ocular objects represented in digital images of human eyes formed and captured using visible-wavelength operating smartphone camera systems, to support automated recognition of various ocular disease (OD) conditions, including dry eye disease (DED), and prescription of subclinical treatment thereof, and thereafter monitor a prescribed course's treatment response, and continue with and/or adapt the prescribed course of treatment with minimal or no human input required, while enhancing the preclinical and/or clinical management of ocular disease conditions around the world.

Another object of the present invention is to provide new and improved automated eye-care diagnostics, treatment & management applications using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide new and improved automated computer-based automated eye-care diagnostic, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated automotive-based eye-care diagnostics, treatment & management applications using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide new and improved automated video-gaming-based automated eye-care diagnostics, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated aircraft-based automated eye-care diagnostic, treatment & management applications using visible-wavelength digital camera systems.

Another object of the present invention is to provide new and improved automated telemedicine eye care system using visible-wavelength digital camera systems.

Another object of the present invention is to provide a novel system network in which a mobile ophthalmic image capturing and processing system is deployed and adapted for automated machine-assisted recognition of ocular disease (OD) conditions, and other anterior eye segment diseases, including automated prescription of disease treatment, and managed assessment of treatment response care.

Another object of the present invention is to provide a novel system network in which a mobile ophthalmic image capturing and processing system is deployed and adapted for automated machine-assisted recognition of dry eye disease and other anterior eye segment diseases, prescription of treatment, and assessment of treatment response, comprising (i) smartphone mobile image capturing systems for use by patients and users whose eyes are to be monitored and cared for using the system, (ii) clinical ophthalmic decision workstations, and real-time ophthalmic image driven ocular disease (DOD) condition recognition engines realized and supported within data centers deployed around the world, (iii) local environmental monitoring stations and third-party data provider platforms, (iv) a global navigation satellite system (GNSS) comprising a constellation of GNSS satellites orbiting around the earth for GPS positioning of objects carrying GNSSS receivers (or GNSS transceivers), (v) a data center supporting web, application and database servers to support the functions and services to be delivered by the system network including a machine-vision driven disease (OD) recognition engine, and (v) a TCP/IP internet infrastructure (i.e. cloud infrastructure) supporting a wireless digital data packet-switched communication infrastructure to support the many systems integrated within the system network.

Another object of the present invention is to provide a novel ecosystem of system networks and medical instruments and systems deployable in a large, medium or small scale hospital management system covering hospitals, clinics and medical practices, including slit-lamp microscope systems, fundus camera systems, fluorescence imaging systems, X-Ray, PET and imaging systems, PCR-based diagnostic and genetic and diagnostic instruments and systems, functional imaging systems, AI and deep-learning medical diagnostic, analytics and decision support systems, and including the mobile ophthalmic image capturing and processing system, that produce and supply patient data for use by the system network of the present invention to support automated ocular disease recognition, treatment and managed care.

Another object of the present invention is to provide a novel mobile ophthalmic image capturing and processing system comprising a system input/output subsystem, a patient history data processing subsystem, a patient eye image data processing subsystem, a patient eye video data processing subsystem, mobile vision testing data processing subsystem, a patient symptom data processing subsystem, a vision functionality data processing subsystem, an environmental factors data processing subsystem, a risk factors data processing subsystem, an exam and in-office testing data processing subsystem, an automated ocular disease (OD) recognition engine, an automated OD treatment prescription engine of the present invention, and a OD treatment compliance data processing subsystem (e.g. treatment compliance engine), configured and integrated into a single system network supporting the innovative remote, mobile and distributed methods of ocular disease diagnosis, treatment and managed care.

Another object of the present invention is to provide a novel digital image and video capturing and processing subsystem comprising an array of mobile smartphone camera systems and an automated digital image processing server realized within one or more enterprise-level data center(s), supporting machine learning and predictive analytics subsystems, a clinical decision support subsystem realized as workstations, tablet computers and laptop workstations, a geolocation (GPS) tracking and linking subsystem to chains of pharmacies, drugstores and health care vendors around the globe, and a database subsystem for storing data and maintaining data records for patients, users, medical and health care providers and medicine vendors across the entire enterprise.

Another object of the present invention is to provide a novel mobile smart phone function used to capture digital images of a patient's eyes using an augmented reality (AR) overlay graphical user interface (GUI) displayed on the screen of the mobile phone, for directing the user to capture images and videos of their eyes in a guided manner to support the advanced machine-vision image capture and processing methods enabled in smart phones used by patients, at workstations in the clinic and hospital setting, and at other remote data centers connected together over a global wireless system network.

Another object of the present invention is to provide a novel machine-vision based digital image processing system employed within the system network, and which is organized, arranged and configured according to traditional machine-vision models employing feature engineering and feature classifiers with shallow structure, so as to classify input digital images as being representative of a particular ocular disease (OD) condition, from many possible trained ocular disease conditions, including dry eye disease (DED) conditions in the human eye.

Another object of the present invention is to provide a novel machine-vision based digital image processing system comprising in its training pipeline, an image/video input module, a pre-processing module, a segmentation module, a post-processing module, feature measurements added to a database, and in a classification pipeline, an image/video input module, an image pre-processing module, an image segmentation module, a post-processing module, a feature extraction module, and a classification/recognition module to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity.

Another object of the present invention is to provide a novel machine-vision based digital image processing system employed within a system network, and is organized, arranged and configured according to a deep learning machine-vision models employing convolutional neural networks having deep structure, so as to provide an end-to-end system for classifying input digital images as being representative of a particular ocular disease (OD) condition.

Another object of the present invention is to provide a novel automated deep neural network module, for implementing a machine-vision based digital image processing system employed within a system network, comprising feature extraction involving convolution and pooling, and classification to produce automated classification outputs, achieved using digital images captured using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide a novel unified framework of deep structure convolutional neural networks (CNNs) specially configured to automatically classify object candidates into specific categories of ocular disease conditions, wherein the deep-learning machine-vision based digital image processing system comprises (i) a faster recurrent-CNN (R-CNN) for carrying out image convolutional layers and producing feature maps from input images, (ii) a regional proposal network (RPN) for producing proposed regions of interest (ROI), and (iii) a faster R-CNN for classifying the proposed ROIs to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity using digital images captured using visible-wavelength digital camera systems.

Another object of the present invention is to provide a novel deep neural network using a TensorFlow 2.0 ecosystem consisting of Python libraries to build an automated deep-learning machine-vision recognition system capable of automatically recognizing specific categories of dry eye conditions from a set of ocular images captured using a standard visible-wavelength operating digital camera system.

Another object of the present invention is to provide a novel deep neural network machine-vision based digital ocular image processing system deployed on a distributed ocular health care system network, employing a hybrid architecture when practicing the system and methods, consisting of (i) a symbolist AI component employing symbolic reasoning and logic (e.g. Rule Based Logic, IF X, then Y), and (ii) a connectionist AI component employing connected statistical models such as machine learning, deep-learning neural networks in computer vision, natural language processing (NLP), and speech recognition, and artificial neural networks (ANNs).

Another object of the present invention is to provide a novel deep neural network method of operating an automated ophthalmic image capturing and processing system configured to enable automated machine-assisted detection and recognition of dry eye disease (DED) conditions in the human eye, automated prescription of disease treatment, and managed assessment of prescribed treatment, by processing a series of digital images of the human eye formed, captured and detected by a mobile visible-wavelength operating digital camera system.

Another object of the present invention is to provide a visible-wavelength mobile smartphone camera system, for use by a patient to capture image data of the patient's eyes and provide the same to the system for image processing using automated machine vision processing to detect and/or measure ocular conditions, including but not limited to, tear meniscus height (TMH), conjunctival injection (CI), scruff at eyelashes, meibomian gland dysfunction (MGD), dermatochalasis, and conjunctivochalasis.

Another object of the present invention is to provide a novel ophthalmic image system for automatically detecting and classifying in collected images of the patient's eyes using the machine-vision imaging processing methods operating on digital images of human eyes formed and captured using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide a novel mobile smartphone camera system, for use by a patient to capture video data of the patient's eyes and provide the same to the system for image processing using automated machine vision processing, to automatically detect and measure ocular functions such as, but not limited to, blink rate, blink interval, blink duration, blink speed and partial blinks per minute, that are used in the automated recognition of ocular disease conditions, including dry eye disease conditions in human eyes.

Another object of the present invention is to provide a novel ophthalmic image processing system for automatically detecting and classifying in collected video of the patient's eyes using the machine-vision imaging processing methods of the present invention, to measure blink rate, blink interval, blink duration, blink speed and partial blinks per minute.

Another object of the present invention is to provide a novel ophthalmic image processing system that uses primary patient diagnostic factors to automatically recognize the type or class of dry eye disease, and its grade/severity experienced by the patient, automatically determined using the machine-vision imaging processing methods operating on digital images of human eyes formed and captured using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide a novel ophthalmic image processing system that employs an automated machine-vision based dry eye disease recognition engine of the present invention, operating on digital images of human eyes formed and captured using visible-wavelength operating digital camera systems.

Another object of the present invention is to provide a novel system network, in which a mobile digital camera system is used by a patient to receive a dry eye disease treatment prescription generated by the system using automated machine vision processing supporting the automated measurement of ocular conditions such as, but not limited to, tear meniscus height (TMH), conjunctival injection, scruff at eyelashes, meibomian gland dysfunction, dermatochalasis, and/or conjunctivochalasis.

Another object of the present invention is to provide a novel ophthalmic image processing system, in which a mobile digital camera system is used by a patient to capture data about the patient and the patient's eyes and provide the same to the system for automated image processing using an automated machine-vision processing system and methods.

Another object of the present invention is to provide a novel mobile smartphone camera system used by a patient to capture digital image and video data about the patient's eyes, and provide the same to an automated machine-vision processing system for automated machine-vision processing.

Another object of the present invention is to provide a novel ophthalmic image processing system for use in automatically monitoring and supporting automated and/or semi-automated decisions and prescription updates for effective treatment and remotely managed care of the dry eye disease condition automatically recognized by the machine-vision imaging processing methods.

Another object of the present invention is to provide a novel automated machine-vision based dry eye disease recognition engine for use in continuously monitoring the patient's ocular disease condition and response to prescribed treatment.

Another object of the present invention is to provide a novel ophthalmic image processing system, wherein compliance factors that are continuously monitored, updated and supplied to the system of the present invention to automatically measure prescribed treatment compliance and support decisions and prescription updates for effective treatment of an ocular disease condition automatically recognized by the machine-vision imaging processing methods.

Another object of the present invention is to provide a novel automated machine-vision based dry eye disease recognition engine for use in monitoring the patient's ocular disease condition and response to prescribed treatment.

Another object of the present invention is to provide a novel dry eye disease (DED) recognition engine deployed within a system, wherein the various detectable ocular/patient factors that are known to contribute to each different class of dry eye disease conditions that the automated DED condition recognition engine of the present invention is configured to automatically recognize during system operation.

Another object of the present invention is to provide a novel automated dry eye disease (DED) condition recognition engine, wherein each rule specifies the various input ocular factors which, if detected by the system for a given set of patient images, then the system will automatically classify the patient images as indicating a particular dry eye disease condition present in the eyes of the patient.

Another object of the present invention is to provide a novel dry eye disease (DED) recognition engine deployed within a system, wherein the various detectable ocular/patient factors that are known to contribute to the specific grading of the severity of a dry eye disease condition classified by the automated DED condition recognition engine.

Another object of the present invention is to provide a novel automated dry eye disease (DED) condition recognition engine, wherein each rule specifies the various input ocular factors which, if detected by the system for a given set of patient images, then the system will automatically specify the grade of severity to be automatically assigned to the particular dry eye disease condition automatically recognized by the system.

Another object of the present invention is to provide a novel dry eye disease (DED) treatment compliance engine deployed within a system having various system inputs continuously supplied to the engine from the treatment prescription engine and various subsystems supporting the system, and the system outputs of the compliance engine being vector inputs to the treatment prescription engine that is being continuously reconfigured as needed to provide optimal treatment to the automatically recognized dry eye disease condition recognized by the system.

Another object of the present invention is to provide a novel automated dry eye disease (DED) treatment compliance engine, wherein each rule specifies the various input ocular factors which, if detected by the system for a prescribed course of dry eye disease treatment, then the system will automatically specify how the prescribed course of treatment should be modified if at all, given the treatment response data factors provided to the system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network configured to support and enable automated detection and measurement of conjunctival injection (CI) of eyes photographically represented in digital images of a front view of human eyes formed, captured and detected by mobile smartphone camera system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system, so as to automatically detect and measure conjunctival injection present in the human eye of a patient at a specific time and date to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on the system network to support and enable automated detection and measurement of tear meniscus height (TMH) of eyes photographically represented in digital images of a front view of human eyes formed, captured and detected by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of automatically detecting dry eye disease conditions, including measuring the retention of tears at the interface between the corneal surface and the eyelid of the human eye, via the meniscus effect, to form a collection of tears having a tear meniscus height (TMH) measured by the method.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system, so as to automatically detect and measure the tear meniscus height (TMH) in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of meibomian gland dysfunction (MGD) and scruff at the eyelashes of the human eyes photographically represented in digital images of a front view of human eyes formed, captured and detected by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of processing digital images of human eyes so as to indicate (i) where meibomian gland dysfunction (MGD) and scruff at the eyelashes are detected in the photographic image of the human eye, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using a machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect meibomian gland dysfunction (MGD) and scruff, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect meibomian gland dysfunction (MGD) and scruff at the eyelashes in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of conjunctivochalasis of the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of automatically processing digital images of a patient human eyes so as to indicate (i) where conjunctivochalasis is detected in the photographic image of the human eyes, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using a machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect conjunctivochalasis, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel method of automatically detecting dry eye disease conditions, and showing the existence of conjunctivochalasis on the lower eyelid of the human eye, preventing the normal replenishment of tears at the meniscus interface between the cornea surface and the lower eyelid.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect conjunctivochalasis in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of dermatochalasis of the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of processing digital images of a patient's human eyes so as to indicate (i) where dermatochalasis is detected in the upper eyelids of the human eyes in the photographic image, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using a machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect dermatochalasis, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect a dermatochalasis condition in the human eyes at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of tear film dynamics (TFD1) of the human eyes photographically represented in a time series set of digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of automatically recognizing dry eye disease (DED) conditions, (i) where light reflective particles are detected in the photographic image of the tear film of a human eye between eye blinks, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using a machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect the light reflective particles in the tear film of a human eye during eye blinks, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect and measure tear film dynamics in human eyes at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of tear film dynamics (TFD2) of human eyes photographically represented in a time series set of digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of recognizing dry eye disease (DED) conditions, (i) where the tear film dynamics (TFD2) of the tear film on human eyes are detected in the human eyes in a series of digital photographic images, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using a machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect and measure the tear film dynamics, by virtue of detected perturbations in concentric placido light discs projected on the cornea from the display screen of a mobile smartphone, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel method of projecting concentric placido light discs onto the human eye from the display screen of a smartphone camera system capturing images of the human eyes during tear film dynamics testing.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect and measure tear film dynamics in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement of tear film dynamics (TFD3) of the human eyes photographically represented in a times series of digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel method of recognizing dry eye disease (DED) conditions, (i) where the tear film dynamics in the human eye are measured by analysis of eye blinks during a time series of digital photographic images, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using the machine-based ophthalmic image processing engine operating on one or more front view digital images of the human eye, so as to automatically detect and measure tear film dynamics of the human eyes, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system, and processed to automatically detect and measure the tear film dynamics in the human eye at a particular time and date by real-time changes in the measurement of tear meniscus height (TMH) in between eye blinks, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network designed to support and enable automated detection and measurement and mapping of the speed of blinking in the human eyes photographically represented in a times series of digital images (e.g. video recording) of a front view of human eyes formed and captured by a mobile smartphone camera.

Another object of the present invention is to provide a novel method of automated recognition of dry eye disease (DED) conditions, (i) where the speed of eye blinks are detected, and measured in the human eye in the photographic image, and (ii) which pixel region of interest (ROI) requires pixel data processing according to a method of digital image processing using the machine-based ophthalmic image processing engine of the present invention operating on one or more front view digital images of the human eye, so as to automatically map changes in eye blinking speed, as an indication of ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system, so as to automatically detect the speed and patterns of eye blinking in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network to support and enable automated detection and measurement of corneal abrasions in the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network, to support and enable an automated method (M10) of detection and measurement of corneal abrasions scratches in the human eyes photographically represented in digital images of a front view of human eyes formed, captured and detected by a mobile smartphone camera system configured and operating in accordance with the principles of the present invention.

Another object of the present invention is to provide a new and improved machine-vision based method of processing time and date stamped digital images of a human eye formed, captured and detected by a mobile smartphone camera system and processed to automatically detect and measure corneal abrasions in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network, to support and enable an automated method (M11) of method of detection and measurement of palpebral fissure (PF) in the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system in accordance with the principles of the present invention.

Another object of the present invention is to provide a novel machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system and processed to automatically detect palpebral fissure (PF) in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) of the present invention configured and trained for use and deployment on a system network, to support and enable automated method (M12) of detection and measurement of the margin reflex distance (MRD) in the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a new and improved machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system and processed to automatically detect margin reflex distance (MRD) in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a new and improved deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on the system network, to support and enable automated method (M13) of detection and measurement of scleral show (SS) of the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a new and improved machine-vision based method of processing time and date stamped digital images of a human eye captured by a mobile smartphone camera system and processed to automatically detect scleral show (SS) in the human eye at a particular time and date, to support automated ocular disease recognition, treatment and manage care, including predicting dry eye disease (DED) class and grade of severity by the automated system.

Another object of the present invention is to provide a new and improved deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network, to support and enable an automated method (M14) of detection and measurement of levator function (LF) in the human eyes photographically represented in digital images of a front view of human eyes formed and captured by a mobile smartphone camera system.

Another object of the present invention is to provide a new and improved machine-vision based method of

CLAIMS

Claims ( 13 )

What is claimed is:

1 . A cloud-based system network for (i) machine-vision supported recognition, treatment and management of ocular conditions detected in the one or both eyes of human beings being treated as patients, specifically for dry eye disease (DED) conditions detected in the eyes of the human beings and severity of the detected DED conditions, and (ii) delivery of ocular care to the human beings as patients situated remotely on the Earth, including prescription of treatment and assessment of treatment response, said cloud-based system network comprising:

a plurality of mobile image capturing systems, selected from the group consisting of mobile smartphone camera systems, mobile camera systems and mobile image capturing devices, each said mobile image capturing system having an integrated camera subsystem with image formation optics and an optical sensor for forming and detecting said digital images using visible-wavelength sources of light illumination, and a graphical user interface (GUI) display surface supporting communication and augmented reality (AR) with a patient and/or user whose eyes are to be monitored using said integrated camera subsystem, and cared for using services supported over said cloud-based system network; a data center supporting web, application and database servers connected to infrastructure of the Internet, to support functions and services to be delivered to patients and users by said cloud-based system network including an automated machine-vision driven ocular disease (OD) recognition engine; a geolocation (GPS) tracking and linking subsystem for GPS-specified location tracking of systems and subsystems and workstations deployed on said cloud-based system network; wherein said automated machine-vision driven ocular disease (OD) recognition engine comprises: an automated digital image processing server realized within said data center, supporting processing of said digital images of said human eyes, and one or more clinical ophthalmic decision support subsystems realized as workstations, tablet computers and/or laptop workstations; and a database subsystem for storing data and maintaining data records for patients and users across the cloud-based system network; wherein, in order to realize automated recognition of dry eye disease (DED) conditions present in human eyes, said automated machine-vision driven ocular disease (OD) recognition engine supports and enables one or more methods of automated detection and/or measurement selected from the group consisting of: an automated detection and/or measurement of hyperemia of the human eyes; an automated detection and/or measurement of tear film in the human eyes; an automated detection and/or measurement of oily lid margin in the human eyes; an automated detection and/or measurement of capped meibomian glands in the human eyes; an automated detection and/or measurement of conjunctivochalasis in the human eyes; an automated detection and/or measurement of eyelash crusting in the human eyes; and an automated detection and/or measurement of eyelid redness in the human eyes; wherein said automated machine-vision driven ocular disease (OD) recognition engine comprises a deep neural network operating to process a series of digital images of the human eye formed, captured and detected by said mobile image capturing system, so as to enable automated machine-assisted recognition of (i) detected ocular disease (OD) conditions in the human eye including dry eye disease (DED) conditions and the severity thereof, and also the automated treatment and management of the detected ocular disease (OD) conditions; wherein said automated machine-vision driven ocular disease (OD) recognition engine includes deep-learning machine-vision models employing multi-layer convolutional networks trained using digital images of eyes captured using visible wavelengths of light illumination, to provide an end-to-end system for processing and recognizing input digital images captured by said mobile image capturing system and indicating particular ocular diseases (OD), including dry eye disease (DED); wherein said automated machine-vision ocular disease (OD) recognition engine further processes patient symptom data, and other ocular/patient factors, that contribute to specific grading of the severity of the dry eye disease (DED) condition recognized by said automated machine-vision driven ocular disease (OD) recognition engine; wherein machine-based logic packages are installed and operative within said automated machine-vision driven ocular disease (OD) recognition engine and include (i) diagnostic logic for determining ocular diseases (OD) based on disease diagnosis input factors provided to as input to said automated machine-vision driven ocular disease (OD) recognition engine, (ii) treatment logic for determining prescription of one or more of the ocular disease (OD) treatment and management recommendations for each ocular disease (OD) condition automatically determined by said automated machine-vision driven ocular disease (OD) recognition engine, and (iii) management logic for managing treatment of each recognized ocular disease condition recognized by said automated machine-vision driven ocular disease (OD) recognition engine; and wherein said one or more clinical ophthalmic decision support subsystems are operably connected to the Internet infrastructure, for use by eye care professionals, including doctors and medical technicians, for the purpose of reviewing and managing patient records maintained by said cloud-based system network and providing patient decision support in connection with delivery of dry eye disease (DED) diagnosis, treatment and management care services to human patients situated remotely on the Earth.

2 . The cloud-based system network of claim 1 , wherein each said mobile image capturing system is a mobile smartphone camera system, and said automated machine-vision driven ocular disease (OD) recognition engine supports practice of a digital imaging processing method for automatically recognizing dry eye disease (DED) conditions in humans comprising the steps of:

(a) automatically processing 2D in vivo digital images of the human eyes formed, captured and detected using visible-wavelength sources of light illumination and said mobile image capturing system; (b) using said automated machine-vision driven ocular disease (OD) recognition engine for automatically processing said digital images and automatically detecting presence and location of specific objects in ocular surfaces in the human eye; and (c) confirming that specific conditions have been satisfied during said image processing to support automated dry eye disease (DED) condition recognition.

3 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine supports displaying an augmented reality (AR) overlay GUI on the display screen of said mobile image capturing system for guiding capture of said digital images of the human eyes using visible wavelengths of light and said mobile image capturing system, and processing the digital images of the human eyes to detect and recognize dry eye disease (DED) conditions in the patient's eyes.

4 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine comprises a deep neural network for building a deep-learning machine-vision recognition system capable of recognizing specific categories of ocular conditions (OD), including dry eye disease (DED), from a set of digital ocular images formed, captured and detected by said mobile image capturing system.

5 . The cloud-based system network of claim 4 , wherein said deep neural network operating to process a series of digital images of the human eye formed, captured and detected by said mobile image capturing system, so as to enable automated machine-assisted recognition of (i) dry eye disease (DED) conditions in the human eye and the severity thereof, and (ii) prescription of treatment of the dry eye disease (DED) conditions.

6 . The cloud-based system network of claim 4 , said deep neural network operating to process a series of digital images of the human eye formed, captured and detected by said mobile image capturing system, so as to enable automated machine-assisted recognition of (i) dry eye disease (DED) conditions in the human eye and the severity thereof, and (ii) prescribed treatment of said dry eye disease (DED) conditions, and (iii) assessment of said prescribed treatment.

7 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine uses patient symptom data to automatically recognize a grade/severity of the dry eye disease (DED) condition(s) experienced by the patient.

8 . The cloud-based system network of claim 1 , wherein said mobile image capturing system is a mobile smartphone system configured for use by a patient to receive a dry eye disease (DED) diagnosis and course of prescribed treatment generated by said automated machine-vision driven ocular disease (OD) recognition engine.

9 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine is configured for use in automatically monitoring and supporting decisions and prescription updates for effective treatment of the dry eye disease (DED) condition automatically recognized by said automated machine-vision driven ocular disease (OD) recognition engine.

10 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine is configured for use in continuously monitoring the patient's DED condition and response to prescribed treatment, using said mobile image capturing system for forming and detecting digital images of patient's eyes that are processed by said automated machine-vision driven ocular disease recognition engine using visible-wavelength sources of light illumination.

11 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine is configured so that compliance factors are continuously monitored, updated and supplied to the cloud-based system network so as to automatically measure prescribed treatment compliance and support decisions and prescription updates for effective treatment of a recognized dry eye disease (DED) condition.

12 . The cloud-based system network of claim 1 , wherein said automated detection and measurement of hyperemia (i.e. conjunctival injection) of the human eyes employs one or more methods selected from the group consisting of a method of detecting conjunctival injection, a method of detecting subconjunctival hemorrhage, a method of detecting episcleritis/scleritis, and a method of detecting superior limbic keratoconjunctivitis;

wherein said automated detection and measurement of tear film in the human eyes employs a method of detecting tear meniscus height; wherein said automated detection and measurement of oily lid margin in the human eyes employs a method of detecting meibomian gland dysfunction; wherein said automated detection and measurement of capped meibomian glands in the human eyes employs a method of detecting meibomian gland dysfunction; wherein said automated detection and measurement of conjunctivochalasis in the human eyes employs a method of detecting conjunctivochalasis; wherein said automated detection and measurement of eyelash crusting in the human eyes employs a method of detecting blepharitis; and wherein said automated detection and measurement of eyelid redness in the human eyes employs a method of detecting chalazion/stye, a method of detecting eyelid cysts and a method of detecting preseptal cellulitis.

13 . The cloud-based system network of claim 1 , wherein said automated machine-vision driven ocular disease (OD) recognition engine employs convolutional neural networks (CNNs) configured and trained for use and deployment on said cloud-based system network, and designed to support and enable automated detection and measurement of dry eye disease (DED) condition in the human eyes photographically represented in digital images of human eyes formed, captured and detected by said mobile image capturing system.

US17/220,535

2021-04-01

2021-04-01

Method of and system for automated machine-assisted detection of ocular disease conditions in human eyes captured using visible illumination light sources and digital camera systems

Active

2043-09-23

US12697024B2

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Method of and system for automated machine-assisted detection of ocular disease conditions in human eyes captured using visible illumination light sources and digital camera systems

EP22782269.9A

EP4312714A4

( en )

2021-04-01

2022-03-31

METHOD AND SYSTEM FOR AUTOMATED MACHINE-ASSISTED DETECTION OF DRY EYE CONDITIONS IN HUMAN EYES CAPTURED USING VISIBLE ILLUMINATION AND DIGITAL IMAGE CAPTURE DEVICE SYSTEMS

AU2022246898A

AU2022246898A1

( en )

2021-04-01

2022-03-31

Automated machine-assisted detection of dry eye disease conditions in human eyes captured using digital camera systems

PCT/US2022/022967

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2021-04-01

2022-03-31

Method of and system for automated machine-assisted detection of dry eye disease (ded) conditions in human eyes captured using visible illumination and digital camera systems

CA3214966A

CA3214966A1

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2021-04-01

2022-03-31

Method of and system for automated machine-assisted detection of dry eye disease (ded) conditions in human eyes captured using visible illumination and digital camera systems

CN202280039371.3A

CN118215429A

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2021-04-01

2022-03-31

Automated machine-assisted detection of dry eye disease pathology in human eyes captured using a digital camera system

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Method of and system for automated machine-assisted detection of ocular disease conditions in human eyes captured using visible illumination light sources and digital camera systems

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Method of and system for automated machine-assisted detection of ocular disease conditions in human eyes captured using visible illumination light sources and digital camera systems

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