Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care.

Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Int Dent J . 2026 Apr 2;76(3):109510. doi: 10.1016/j.identj.2026.109510 Search in PMC Search in PubMed View in NLM Catalog Add to search Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care Yuqian Feng Yuqian Feng 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China Find articles by Yuqian Feng 1 , Yifan Jia Yifan Jia 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China Find articles by Yifan Jia 1 , Linhe Lv Linhe Lv 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China Find articles by Linhe Lv 1 , Xiaoxi Wei Xiaoxi Wei 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China Find articles by Xiaoxi Wei 1, ⁎ , Min Hu Min Hu 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China Find articles by Min Hu 1, ⁎ Author information Article notes Copyright and License information 1 Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China ⁎ Corresponding authors. Department of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, Jilin, 130021, P. R. China. [email protected] [email protected] Collection date 2026 Jun. © 2026 The Authors This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13084668  PMID: 41932097 Abstract Introduction and aims For decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dimensional (3D) imaging technologies. Nevertheless, single-modal data exhibits inherent limitations, including insufficient spatial resolution, lack of tissue specificity, and discontinuous spatiotemporal information. These drawbacks make it challenging to meet the increasingly high requirements for precision and comprehensiveness in modern clinical decision-making. Multimodal data fusion technology, by integrating diverse types of medical data, can fully leverage the unique advantages of each modal data while compensating for their respective deficiencies. This review aims to systematically elaborate on the application value and research progress of multimodal data fusion technology in orthodontics, and to provide insights into its future development directions. Methods A review of the contemporary clinical literature, relating to multimodal data fusion technology, was undertaken using information obtained from the electronic databases PubMed and Web of Science. Results Eight types of commonly used modal data in orthodontics were identified, each with distinct characteristics. Multimodal data fusion technologies have been widely applied in orthodontic diagnosis and treatment, demonstrating significant application value. Moreover, such technologies have made notable progress in promoting multidisciplinary collaborative treatment involving orthodontics, oral surgery, and prosthodontics. The core advantage of multimodal data fusion technology lies in constructing comprehensive analytical models integrated with multidimensional biomechanical parameters, which can provide quantitative tools for dynamic orthodontic treatment monitoring and outcome evaluation. Conclusion Multimodal data fusion technology holds remarkable clinical value in the field of orthodontics. It not only supports precise orthodontic treatment but also enhances interdisciplinary collaboration through standardized data platforms. To further advance efficient and precise oral healthcare, future efforts should focus on improving the fusion accuracy of multimodal data and integrating more types of dynamic data. Clinical Relevance The application of multimodal data fusion technology can effectively address the limitations of single-modal data in orthodontic practice, providing clinicians with more comprehensive and accurate diagnostic information. It facilitates dynamic treatment monitoring and precise outcome evaluation, thereby optimizing clinical decision-making. Additionally, the standardized data platform established by this technology promotes seamless collaboration between orthodontists, oral surgeons, prosthodontists, and other specialists, contributing to the improvement of overall oral healthcare quality. Key words: Orthodontics, Multimodal imaging, Artificial intelligence, Deep learning, Patient simulation Introduction The conventional orthodontic diagnostic and treatment paradigm relies on intraoral occlusal analysis, plaster model examinations, and relevant measurements to formulate therapeutic plans, which are aimed at restoring or improving patients’ occlusal function and safeguarding the health of their orofacial system. With the development of technology, Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) techniques have realized the three-dimensional structural reconstruction of hard and soft tissues of the craniomaxillofacial complex, while intraoral scanning systems and 3D facial imaging devices acquire radiation-free digitalized dentition and facial morphology data. These technological breakthroughs not only optimize treatment planning workflows but also provide quantitative benchmarks for dynamic therapeutic monitoring. It is worth noting that the clinical application of functional data acquisition devices (such as the mandibular movement trajectory tracing system, etc.) has realized the four-dimensional dynamic analysis of the jaw position relationship and the mandibular movement pattern, marking that the assessment of oral function has entered the stage of precise quantification, and enabling the evaluation of dynamic data to be incorporated into the diagnostic system. However, current clinical practices reveal inherent limitations in single-modality data evaluation methodologies, primarily manifested in insufficient three-dimensional spatial resolution, lack of tissue-specific contrast resolution, and spatiotemporal discontinuity in information integration. To construct a comprehensive biomechanical model of the craniomaxillofacial system, clinicians typically require multisource data collection and integration for diagnostic and therapeutic decision-making – a process prone to information discrepancies and diagnostic blind spots. Consequently, the integration of heterogeneous medical datasets, termed multimodal data fusion, has become imperative. This technology enables multidimensional reconstruction of anatomical structures and dynamic simulation of biomechanical environments through synergistic aggregation of diverse data modalities. By enhancing diagnostic comprehensiveness and enabling cross-validation of biomechanical parameters, multimodal fusion reduces diagnostic uncertainty while systematically mitigating risks of clinical misjudgement. Orthodontic treatment frequently necessitates multidisciplinary collaboration, requiring orthodontists to engage in systematic interdisciplinary consultation with specialists across disciplines both prior to and during treatment phases to develop comprehensive therapeutic strategies. Within the realm of interdisciplinary collaborative care, multimodal data integration demonstrates unique clinical value. The three-dimensional visualization platform based on mixed reality technology not only enables collaborative diagnosis and treatment among orthodontists and experts from multiple disciplines such as prosthodontics, implantology, and maxillofacial surgery, but also provides quantitative decision-making support for key treatment objectives such as the determination of vertical dimension and smile aesthetic design for complex cases through the virtual occlusion reconstruction and dynamic aesthetic prediction system. This study systematically reviews the application progress of various modal data and the fusion technology among them in orthodontic clinical practice and multidisciplinary collaborative treatment ( Figure 1 ). Fig. 1. Open in a new tab The application progress of various modal data and the fusion technology among them in orthodontic clinical practice and multidisciplinary collaborative treatment. Multiple modal data Dental photographs As a non-invasive imaging modality with high objectivity, intraoral digital photography preserves biometric data across different treatment phases and provides visual documentation throughout the diagnostic and therapeutic continuum. This technology facilitates quantitative analysis of three-dimensional anatomical structures during preoperative planning (including linear measurements, angular calculations, and soft tissue proportion assessments), real-time intraoperative monitoring, and longitudinal postoperative comparisons to validate therapeutic outcomes. 1 It serves as a standardized visual medium for clinician-patient communication, interdisciplinary consultations, and academic exchanges among professionals, while also holding evidentiary value in legal contexts. 2 In aesthetic evaluation domains, digital camera photos can be used for smile aesthetic evaluation 3 and facial attractiveness analysis. 4 Advanced digital image processing techniques allow simulation of various treatment parameters’ potential impacts on dentofacial aesthetics. 5 Technological innovations, particularly the application of deep learning algorithms, have achieved automated occlusal analysis 6 and dynamic soft tissue monitoring, 7 significantly enhancing the objectivity and reproducibility of orthodontic practice. These integrated technological advancements endow intraoral digital photography with dual clinical utility and research innovation potential. X-ray film Since the mid-20th century, two-dimensional X-ray imaging techniques such as panoramic radiographs and cephalometric posteroanterior/lateral radiographs have been used as routine methods to acquire information about teeth and craniomaxillofacial bones. Panoramic radiography (introduced into clinical practice in the late 1930s) enables systematic evaluation of dental anomalies (caries, impacted teeth, congenital missing teeth), root morphological parameters (length/curvature), vertical alveolar bone height, maxillary sinus pathologies, and jaw cystic/tumorous lesions. 8 Posteroanterior cephalograms (standardized in the 1950s) provide diagnostic evidence for skeletal asymmetry by quantifying craniofacial transverse symmetry and maxillomandibular basal arch width ratios. The lateral cephalometric radiograph (introduced into the orthodontic field by Broadbent in 1931), as a core diagnostic tool for analysing the mechanism of malocclusion, determining the growth and development period, evaluating the sagittal relationship of the jawbone, and monitoring the treatment effect, has gained international recognition for its technical value. Cephalometric analysis based on lateral cephalograms has evolved through three historical phases: 1) Traditional tracing phase (1931-1980): Manual landmark identification using acetate tracing sheets. 2) Digital analysis phase (1980-2010): Semi-automated landmark localization via specialized software (eg, Dolphin Imaging 9 ), reducing analysis time per case from 45 minutes to 15 minutes. 3) Intelligent analysis phase (2010-present): Based on fully automated marker point recognition driven by artificial intelligence (AI), which compresses analysis time to 2 seconds/case, coupled with visualized decision-support systems featuring growth prediction and treatment simulation capabilities. 10 , 11 Cone beam computed tomography The inherent limitations of two-dimensional radiographic techniques – including anatomical superimpositions, image distortion, deformation, and artifacts – compromise measurement accuracy, 12 which has driven the clinical adoption of computed tomography (CT, 1972) and its dental-optimized counterpart, cone beam computed tomography (CBCT, 1982). Compared to conventional CT, CBCT delivers superior imaging precision with shorter scan times and reduced radiation exposure, enabling three-dimensional visualization and quantitative analysis of craniofacial structures. 13 It has emerged as an advanced diagnostic modality for evaluating alveolar bone remodelling and root resorption, 14 cephalometric analysis, 15 upper airway assessment, 16 and temporomandibular joint evaluation. 17 CBCT datasets are further leveraged for extended applications, including three-dimensional visualization tracking of the craniomaxillofacial region before and after treatment, 18 digital surgical guide design, 19 and personalized orthodontic appliance development. 20 The clinical translation of three-dimensional imaging technologies has substantially enhanced the precision of pathological-anatomical feature identification and improved procedural controllability throughout orthodontic treatment workflows. Magnetic resonance imaging Temporomandibular disorders (TMD), as a category of craniofacial functional disorder syndromes caused by multiple factors, has shown an increasing incidence trend year by year, reaching as high as 5% to 12%. 21 In the field of imaging evaluation, although CBCT can intuitively observe the hard tissue structures of the temporomandibular joint in three dimensions, this technology is limited by the reduction of the signal-to-noise ratio caused by inherent image artifacts and the deficiency of soft tissue contrast resolution, which restricts its application in the comprehensive evaluation of the joint complex. In contrast, magnetic resonance imaging (MRI), leveraging its non-invasive nature and superior soft tissue discrimination, has emerged as the clinical gold standard for temporomandibular joint (TMJ) evaluation. This modality not only enables non-invasive visualization of spatial relationship variations in the disc-condyle complex but also facilitates precise identification of masticatory muscle pathologies and joint effusion. 22 Notably, MRI applications now extend to functional airway assessment, where three-dimensional quantification of soft palate morphology and upper airway volumetry provides objective data for risk stratification of complications such as obstructive sleep apnoea syndrome, thereby supporting optimized multidisciplinary treatment planning. 23 Dental models and intraoral scanners As a core element in orthodontic diagnosis and treatment, the morphological characteristics of the dentition are not only the direct target of clinical intervention but also the fundamental source of data for formulating individualized orthodontic treatment plans. Within traditional diagnostic frameworks, plaster models have long served as the standard archival medium for three-dimensional dentoalveolar information due to their favourable cost-effectiveness ratio and morphological reproduction. However, they are difficult to preserve and achieve information sharing. With the innovation of digital technologies, the three-dimensional model reconstruction technology of the dentition has gradually become a research hotspot. It first emerged in the 1980s, and in 2008, the first office digital impression system for full-arch scanning was launched on the market. Since then, three-dimensional data acquisition devices represented by intraoral scanners (IOS) and desktop scanners (DS) have emerged and been iteratively updated. IOS technology reconstructs orofacial hard and soft tissues through a tripartite workflow: data acquisition, point cloud optimization, and surface mesh reconstruction, achieving precision equivalent to conventional impressions. 24 Meanwhile, it can reduce the incidence of side effects such as nausea, support cloud storage and remote sharing of information, and has evolved into a multimodal diagnostic platform. This includes early caries diagnosis (with a sensitivity of 92.3%), evaluation of tooth wear indices, and classification of gingival biotypes. 25 , 26 When combined with relevant software, it enables tooth arrangement experiments and simulation of post-treatment effects. 27 Moreover, by integrating additive manufacturing technology, it achieves the precise manufacturing of physical orthodontic devices (such as functional appliances, clear aligners, and personalized lingual brackets). Face scan While planar photogrammetry enables quantitative analysis of facial linear parameters, its 2D nature limits it from acquiring depth data, reducing accuracy and reliability. Direct anthropometry (DA) is cost-effective but may introduce errors due to its time-consuming process and the contact-induced deformation of soft tissues caused by measuring tools. With advances in digital technology, 3D facial imaging has gradually achieved clinical translation, primarily encompassing three core technologies: laser scanning, stereophotography, and structured light imaging. These technologies efficiently and accurately reproduce facial morphology, texture, and colour, facilitating precise facial quantitative assessment, soft tissue biomechanical analysis, and prediction of morphological changes. 28 , 29 , 30 Furthermore, advancements in four-dimensional (4D) scanning systems now enable dynamic facial feature capture. Commercial systems based on multi-view stereo vision technology exemplify this progress, precisely recording trajectories of mimetic muscle movements, speech-related functional states, and dynamic smile characteristics. These innovations support the development of biomechanical databases, quantitative assessment of nasolabial motion ranges, and facial biometric modeling. 31 Virtual facebow and jaw motion tracking system Contemporary orthodontics has established a multi-dimensional treatment framework covering aesthetic optimization, functional reconstruction of the stomatognathic system, and biomechanical balance of the temporomandibular joint. In this paradigm, alongside static anatomical parameters, dynamic biomechanical parameters of mandibular functional movements have become core to clinical diagnosis and treatment monitoring. Traditionally, clinicians transferred jaw relationships to physical articulators via mechanical facebows and simulated occlusion based on empirical condylar movement ranges, which suffered from limited spatial accuracy and poor adaptation to individual biological differences. With advances in 3D digital technology, virtual articulators, mandibular trajectory analysers, and optical dynamic capture systems have been clinically applied. Through parametric modelling of condylar trajectories and fusion of real-time movement data, a 4D dynamic patient model has been successfully constructed. 32 Virtual reality and augmented reality technologies Nowadays, computer-aided technologies are advancing rapidly in stomatology, with virtual reality (VR) and augmented reality (AR) increasingly applied in student education and clinical processes. Via VR learning software simulating treatments, students can gain clinical operation experience in virtual environments, greatly facilitating dental education. 33 VR can also help construct virtual reality patient models (VRPM) for simulating orthognathic surgeries, educate patients to identify diseases, and enable timely health management. With smart toothbrushes, app-based mobile monitoring, or AR glasses, it can improve oral hygiene, especially among children. In the future, more functions may be developed to assist in teaching, diagnosis and treatment, and disease management, providing more convenient tools for medicine. Modern orthodontic diagnosis and treatment require integrating multi-source heterogeneous data for multi-dimensional treatment planning, efficacy prediction, and dynamic evaluation. Digital platforms demonstrate significant advantages in visual interaction of doctor-patient information. However, the existing technical system still faces challenges such as insufficient standardization of data collection and incomplete dynamic analysis algorithms. It is urgent to break through current technical bottlenecks through machine learning algorithm optimization and multi-modal data fusion technology, ultimately achieving a visualized intelligent diagnosis and treatment closed-loop under the precision medicine framework. Multimodal data fusion technology Single-modal medical imaging data provides limited information and fails to meet the increasingly precise diagnostic and treatment needs, making image fusion research in the medical field a hot topic in recent years. Multimodal data fusion refers to processing at least two different types of data through computer digitization and medical image registration, matching their internal and external features in spatial coordinates to generate a new, more comprehensive fused image that reflects more thorough diagnostic and treatment information. In the following, we will briefly introduce the existing multimodal fusion technologies and their applications in the field of stomatology in the order of two-modal, three-modal, and four-modal fusion technologies. The fusion of two modalities Intraoral scanning + CBCT In traditional orthodontic treatment, plaster models or intraoral scans are routinely used for monitoring dental arch morphology. However, the lack of root information may lead to complications such as bone dehiscence. 34 Although cone-beam computed tomography (CBCT) can provide information on the root-bone relationship, its limited spatial resolution and risks associated with ionizing radiation restrict its repeated application in dynamic monitoring. Therefore, constructing a high-precision 3D dentition model by integrating IOS and CBCT data constitutes a key technology for achieving real-time assessment of periodontal status and dynamic monitoring of the root-bone relationship. The specific process includes precise image segmentation of IOS and CBCT, as well as high-precision fusion between them. CBCT data processing employs manual segmentation, semi-automatic segmentation, and deep learning-based fully automatic segmentation methods. Manual segmentation is time-consuming (average 4.6 hours/full jaw). Semi-automatic/automatic methods, using interactive threshold segmentation combined with morphological processing, improve efficiency to 1.2 to 2.5 hours/full jaw but still require manual calibration of key anatomical structures. In recent years, deep learning technologies, especially convolutional neural networks (CNN), have significantly advanced automatic image segmentation. State-of-the-art 3D CNN architectures, including V-Net and 3D U-Net, have evolved from isolated tooth or alveolar bone segmentation 35 to sophisticated simultaneous alveolar bone-dentition segmentation. 36 Notably, Zhang et al 37 improved the root segmentation accuracy to the submillimetre level (RMSE 0.38 mm) by optimizing the voxel size (≤0.25 mm³) and adjusting the dynamic threshold. Concurrently, IOS data processing predominantly utilizes two technical approaches: 1) feature extraction via conventional point cloud segmentation networks (eg, PointNet++, DGCNN), and 2) deep learning frameworks such as MeshSegNet and dynamic graph convolutional networks (DGCNN). Hao et al 38 innovatively introduced an edge-aware loss function, developing the DC-Net model which maintained a segmentation accuracy of 94.7% even under complex dental arch morphologies. Regarding the fusion of CBCT and IOS, the current mainstream fusion strategies can be divided into two categories: The first category is the anatomical structure splicing method, which preserves CBCT-derived root morphology data and performs geometric registration with IOS-acquired coronal structures. 39 , 40 However, discrepancies in mesh density between modalities frequently induce stepwise interface discontinuities at fusion boundaries. The second category is the deformation field mapping method, which employs non-rigid registration algorithms to morphologically align CBCT crown segments with IOS geometries. The basic scheme uses Iterative Closest Point (ICP) rigid registration (registration error 1.2-2.5 mm), but this relies on good initialization. Optimized schemes reduce the initial registration error to 0.8 to 1.3 mm by combining Fast Point Feature Histograms (FPFH) or 4-Point Congruent Sets (4PCS) algorithms, but these depend on similar features between point clouds. 41 Based on this, Ren et al 42 proposed a hybrid registration framework, reducing the registration error to 0.15 to 0.3 mm. Recently, deep learning-based multimodal fusion systems (eg, Liu et al’s 43 architecture) have further enhanced processing efficiency (<8 minutes per full arch), reduced metal artifact interference by 37%, and have been deployed in practical clinical applications, enabling root-bone relationship visualization. Furthermore, Chen et al 44 developed a Spatio-Temporal CNN (ST-CNN)-based cross-temporal multimodal fusion system. This system requires only the initial consultation’s CBCT and IOS data, and collects IOS data during follow-up visits to rapidly visualize root and jawbone information in 3D ( Figure 2 A). It holds promise for enabling root-bone relationship monitoring and continuous risk assessment throughout the entire orthodontic treatment process. Fig. 2. Open in a new tab Integration of Intraoral Scanning and CBCT: A, Schematic diagram of the cross-temporal multimodal fusion algorithm the system consists of the CBCT alveolar bone segmentation module, the CBCT tooth segmentation and feature extraction module, the IOS tooth segmentation and feature extraction module, and the registration module. The former two were applied during the initial diagnosis (a and b), whereas the latter two were performed at each follow-up visit (c, d1, and d2). 44 B, A method to fuse datasets of digital dental casts and CBCT scans using intraoral markers glued to the gingiva. 47 C, A postoperative IOS is a validated alternative to a postoperative CBCT scan for determining implant placement accuracy. 46 This technical system also holds significant application value in implant therapy, combined orthodontic-orthognathic treatment, and periodontal diagnosis. By fusing CBCT data with IOS occlusal contact data, it enables the construction of biomechanically optimized surgical guides (axial deviation < 2°), 45 substitutes for postoperative CBCT scans to assess implant positions ( Figure 2 C), 46 simulates orthognathic surgical procedures, and designs surgical splints, thereby facilitating multidisciplinary collaborative treatment ( Figure 2 B). 47 In addition, Tan et al 48 developed an automatic periodontal diagnosis system (PerioAI). This system can not only simulate existing clinical diagnostic procedures and directly calculate the gingiva-bone distance (GBD) but also provide visual information regarding soft tissue morphology derived from IOS and the severity of bone loss observed in CBCT images. This integration enables the accurate diagnosis of periodontal diseases, overcoming limitations such as diagnostic variability due to differences in clinicians’ experience and the conventional periodontal probing that only assesses 6 sites per tooth. Nevertheless, its diagnostic accuracy, especially for complex cases, still needs further improvement. Photo/Face scan+ CBCT Beyond the aforementioned dentomaxillary system, which is the direct target of orthodontic intervention, the dynamic changes in facial soft tissues before and after treatment, as a core factor influencing patients’ aesthetic evaluation, have gradually become a key consideration in formulating diagnosis and treatment plans. This also serves as an important reference for the design of combined orthodontic and orthognathic treatment protocols. In current three-dimensional imaging technologies, the reconstruction of facial soft tissues obtained by CBCT lacks sufficient morphological fidelity due to the absence of skin texture features. In contrast, digital photography can provide real skin texture data with higher precision. Based on this, integrating high-precision soft tissue surface data with CBCT hard tissue data has become an important technical approach to achieve accurate modelling of maxillofacial hard and soft tissues. Alhammadi et al 49 demonstrated that registration models using non-standardized frontal photographs and CBCT exhibit relative reliability for soft tissue profile measurements. However, limited by the CBCT field of view and positional differences, these models result in registration errors in the glabellar region, leading to insufficient measurement accuracy. Addressing this limitation, Lee et al 50 innovatively applied UV mapping technology. By utilizing surface registration algorithms, they superimposed two-dimensional facial photographs onto the CBCT soft tissue model, thereby providing enhanced aesthetic reference ( Figure 3 A). Fig. 3. Open in a new tab Integration of Facial Scanning and CBCT/IOS: A, Lee implemented UV mapping technology to superimpose two-dimensional facial photographs onto CBCT-derived soft tissue models. 50 B, The Maal research evaluated three registration algorithms following CBCT substitution for spiral CT, thereby establishing a refined clinical registration protocol with enhanced anatomical congruence. 52 C, (a)Scan of perioral area aligned to scan of occlusion rim. (b)Scan of nose and lower part of forehead aligned to scan of occlusion rim. (c)Scans of occlusion rim, perioral area, and nose aligned. (d)Scan of face aligned to scan of nose and lower part of forehead. 55 D, (a) The individual reference tray (IRT) is designed and 3D-printed. (b) The IRT is dedicated to the superimposition of intraoral scans and facial scans. 57 With the iterative development of optical scanning technologies, researchers have begun employing landmark-based partial Procrustes analysis combined with an Improved Iterative Closest Point (HICP) algorithm for registration, enabling the superimposition of stereophotogrammetric data onto spiral computed tomography (CT) scan images and achieving clinically acceptable registration accuracy. 51 However, this method exhibits suboptimal accuracy in the buccal, periorbital, and forehead regions. The error sources involve soft tissue deformation caused by differences in imaging posture (seated position for facial scanners versus supine position for CT), as well as interference from facial muscle activity during prolonged CT scanning times. To address this, the Maal team replaced spiral CT with CBCT and systematically compared three registration algorithms, ultimately establishing a superior clinical registration protocol ( Figure 3 B). 52 Further research has found that fused models are limited to surface registration due to the lack of voxel information in 3D photography, leaving room for improvement in registration accuracy. Subsequent studies suggest integrating intraoral scanning data to break through existing technical bottlenecks, which can not only optimize the accuracy of registration algorithms but also effectively compensate for the inherent deficiencies in CBCT data acquisition of dental crown occlusal surfaces. In the future, how to further overcome registration challenges in complex regions such as the buccal and periorbital areas, balance imaging accuracy and scanning efficiency, while establishing standardized technical application processes and reducing barriers to clinical popularization, is crucial for this technology to move from initial clinical application to widespread and precise implementation. Intraoral scanning + Face scan The construction of an integrated 3D digital model of dentomaxillary hard and soft tissues, by integrating intraoral scanning and 3D facial data, not only supports the diagnosis of complex dentomaxillary deformities and the design of correction plans but also enables visual analysis of implant prostheses, thereby enhancing the scientificity and predictability of orthodontic multidisciplinary treatment. In terms of three-dimensional surface registration technology, the existing methods mainly fall into two technical paths: landmark-based registration and surface-based registration. The former achieves 3D image registration by manually marking three or more corresponding anatomical landmarks, while the latter completes fine registration through topological structure matching of 3D meshes in anatomical regions combined with the Iterative Closest Point (ICP) algorithm. By avoiding errors from manual point selection, it exhibits significant advantages in terms of accuracy and repeatability. To optimize registration accuracy, Campobasso proposed a composite strategy: first establishing an initial coordinate system through three-point registration, then achieving optimal fitting via least squares optimization. Its supporting software can also integrate cephalometric radiographs with dentomaxillofacial scan data, enabling synchronous visualization of anatomical structures and cephalometric data. 53 Given the morphological stability of the forehead and nasal root regions, this area has been established as a reliable reference for registration. 54 Based on this finding, Russo et al 55 developed a new workflow: using an intraoral scanner to acquire 3D data of the occlusal edges, perioral area, and the nasal region below the forehead, and achieving precise dental-facial registration through feature-matching algorithms ( Figure 3 C). Similarly, Elbashti et al 56 used the nasal region as a reference and employed a global best-fit algorithm to control errors within a clinically acceptable range (<0.2 mm). In addition, in the field of dental prosthetics, the Digital Smile Design (DSD) technology enables the visual analysis of the dynamic relationship between the morphology of restorations and facial soft tissues through a virtual patient model ( Figure 3 D). For edentulous patients, this technology can accurately quantify the spatial relationship between the vertical dimension and the profile line, thus improving aesthetic satisfaction. 57 It should be noted that in resource-constrained scenarios, 3D Dense Face Alignment (3DDFA) technology can still achieve clinical-level precision in 3D maxillofacial modelling by fusing 2D images with intraoral data, which provides a feasible path for the inclusive application of digital diagnosis and treatment technologies. 58 CBCT+MRI TMD is a significant cause of maxillofacial deformities and a key therapeutic target that deserves attention. Since the temporomandibular joint (TMJ) complex relies on the synergistic interaction of hard and soft tissues, single-modality imaging cannot achieve comprehensive evaluation of its status. Despite notable differences between CBCT and MRI in voxel size (0.2 mm³ vs 1.0 mm³), signal characteristics (Hounsfield Unit vs T1/T2-weighted signals), and discriminability of anatomical structures, integrating CBCT’s high-precision bony tissue imaging with MRI’s excellent soft tissue imaging via multimodal fusion technology to construct 3D visualization models remains of important clinical value. CT-MRI image registration technology, emerging in the 1990s, has been gradually applied in clinical research on the pelvis, humerus, and other areas, but remains in the exploratory stage for the TMJ region. Existing methods fall into three categories: 1) Manual registration based on cortical bone contours (eg, visual alignment via Photoshop), which is easy to operate but has low inter-operator consistency 59 ; 2) Feature extraction-based automatic registration, whose core workflow includes feature extraction, optimization of spatial transformation matrix, similarity measurement (eg, mutual information), and optimization algorithms; 3) Semi-automatic registration with human intervention, which can reduce registration error to 0.15 ± 0.03 mm through ROI extraction and local fine adjustment. The development of deep learning, particularly CNN, has provided innovative solutions for multimodal registration of CBCT and MRI. Through hierarchical feature extraction, it outperforms traditional methods in terms of similarity measurement. Deep learning algorithms based on mutual information enable end-to-end registration without pre-segmentation, demonstrating high diagnostic consistency. Generative Adversarial Networks (GANs) can extend existing single-modality registration algorithms to multimodal image registration via domain adaptation, effectively overcoming inter-modality differences, with a registration accuracy of 0.12 ± 0.05 mm. 60 Currently, clinical practice mostly adopts hybrid registration strategies ( Figure 4 A), such as the Amira visualization software, which combines rigid registration with the Normalized Mutual Information (NMI) algorithm. 61 First introduced into medical image registration by Maes et al, 62 NMI can avoid large misalignment in field of view (FOV) between images of different modalities. Wang et al 63 successfully achieved multimodal detection of TMJ calcifications using this technique (sensitivity: 93.8%). Additionally, scholars have validated through cases that fused images are highly accurate in diagnosing lesions such as tumours, chronic inflammation, calcifications, and disc displacement. 61 The Al-Saleh team has successively explored various registration techniques and improved inter-examiner consistency to ICC = 0.91 by optimizing spatial transformation parameters (6-degree-of-freedom rigid transformation), while also enhancing the accuracy of novice examiners. 64 , 65 , 66 , 67 , 68 , 69 However, experiments revealed that fused images exhibit lower detection efficiency for subtle bone erosions (<0.5 mm) compared to standalone CBCT, which may be attributed to MRI images masking tiny bony changes in the fused images. 64 Fig. 4. Open in a new tab Integration of CBCT and MRI: A, Fused images from CT and MRI for GCTTS of anterior disc displacement without reduction. 61 B, A data-driven approach to diagnose TMD based on deep learning neural networks. 72 Despite significant advancements in existing technologies, mutual information-based registration methods still suffer from limitations such as high computational complexity and strong reliance on manual input. The emergence of deep learning has enabled automated diagnosis, with current studies leveraging deep learning to diagnose temporomandibular joint disorders using panoramic radiographs, MRI, and CBCT ( Figure 4 B). 70 , 71 , 72 Future research directions should focus on: 1) Developing dedicated network architectures for the TMJ; 2) Establishing multi-centre standardized registration protocols; 3) Integrating dynamic MRI and CBCT data for four-dimensional biomechanical simulations. The fusion of three modalities X-Ray + intraoral scanning + Face scan Without relying on CBCT, the multi-data integration method based on cephalometric coordinate systems can also achieve accurate registration of 3D data of facial soft tissues and dentition, providing a feasible approach for constructing orthognathic surgery models. Noguchi et al 73 projected 3D models of teeth and faces onto computers to match cephalometric measurements. Lemieux et al 74 used this method to match 3D CT cranial data with X-ray films, a pattern described as perspective digitally reconstructed radiography. It enables spatial alignment of multimodal data without pre-set anatomical landmarks. When handling cases with low-curvature facial regions or ambiguous anatomical markers, this technology performs feature matching by quantifying projected contours, significantly reducing registration errors. This not only improves the repeatability of surgical simulations but, more importantly, provides a reliable numerical simulation basis for predicting osteotomy plans in complex maxillofacial deformities. 73 Intraoral scanning + Face scan + CBCT The IOS-FS-CBCT composite 3D model system can simultaneously present high-precision tooth-bone-soft tissue structures, providing core technical support for the entire orthodontic diagnosis and treatment process as well as multidisciplinary collaborative treatments (surgical design, implant biomechanical optimization, and postoperative prediction). At the technical implementation level, Joda et al 75 proposed a registration method based on dental landmarks ( Figure 5 A). However, due to inherent anatomical limitations in facial scanning (eg, insufficient tooth exposure or soft tissue coverage), IOS-FS registration still faces technical bottlenecks. To address this, Park et al 76 innovatively adopted a ‘facial scan tray’ as a registration medium ( Figure 5 B). By combining anatomical landmarks of the nasal tip and medial canthi, they successfully achieved spatial registration of the three datasets. Additionally, for errors caused by soft tissue temporal variability and expression differences, non-transmissive fiducial markers combined with a marking tray can significantly enhance registration accuracy ( Figure 5 C). 77 Fig. 5. Open in a new tab Integration of IOS, Facial Scanning and CBCT: A, Construct a virtual patient, and then use 3D implant software to formulate a plan. 75 B, Practical facial scanning tray for accurate spatial alignment with nasal tip, medial canthus and other reference landmarks. 76 C, Four non-radiopaque markers placed in the midfacial region eliminate registration errors induced by facial expressions and other factors. 77 From a clinical translation perspective, CBCT enables quantitative assessment of key parameters such as alveolar bone height, density, and spatial angle. Through dedicated implant planning software, this facilitates 3D implant positioning optimization and immediate-load provisional prosthesis design. This digital workflow not only shortens clinical operating time but also achieves synergistic optimization of aesthetic and functional treatment goals by enhancing treatment predictability. Intraoral scanning+ CBCT/Face scan+ Jaw motion tracking system The demand for precision in oral diagnosis and treatment is driving the evolution of technologies from static observation to dynamic simulation. Given the biomechanical coupling effects among the components of the cranio-maxillary complex (skull, jawbone, temporomandibular joint, dentition, and muscles), constructing a four-dimensional spatiotemporal model that integrates temporomandibular joint kinematics, occlusal dynamics, and soft tissue deformation data has become an inevitable trend. From the perspective of technological evolution, the accurate acquisition of mandibular kinematic parameters represents the core breakthrough point of trimodal integration. Previously, physicians used facebows to transfer the 3D spatial relationship between patients’ maxilla and hinge axis to articulators, and set the movement range of dentition models on articulators by limiting the motion range of condylar balls. The advancement of digital technology has facilitated the formation of two core paradigms for trimodal modelling: The first paradigm relies on the virtual facebow (VF) and virtual articulator (VA) systems to accurately reproduce the three-dimensional spatial relationship of the cranio-maxillary complex. 78 , 79 For instance, in the field of prosthodontics, some scholars have achieved the alignment of facial and intraoral scans using intraoral scanning bodies fabricated via additive manufacturing, and mounted the virtual maxillary model on a virtual articulator. 80 , 81 This system not only promotes the integrated construction of three-dimensional virtual patients, but also significantly shortens clinical chairside time and laboratory operation cycles, providing an efficient solution especially for the precise prosthodontic treatment of edentulous patients. 82 The second paradigm is the trimodal integration guided by mandibular motion tracking, which encompasses technologies such as mechanical bow tracking, video tracking, stereophotogrammetry, dynamic radiographic imaging, electromagnetic mandibular motion trajectory recording, ultrasonography, and optical motion capture. The acquired individualized motion parameters are fused with data from other modalities using surface/point cloud registration methods ( Figure 6 E). 83 , 84 For example, prosthodontists utilize facial trackers and motion engine software programs to real-time track and record patients’ lip dynamics, which are then combined with intraoral and facial scans to achieve more aesthetically pleasing lip-to-tooth relationships. 85 Fig. 6. Open in a new tab Applications of Three-Modal Data Fusion Involving Dynamic Data: A, Joint movement coordination is evaluated using mandibular motion capture system and CBCT. 86 B, Visualize the recorded axiographs, joint motion, and occlusal contacts in the 3D visualization space of the software program of the jaw tracking system. 87 C, Dynamic 3D images fusion of the temporomandibular joints can visually display mandibular motion trajectories and the relative TMJ positions. 89 D, TMJ kinematic path generated from jaw tracking technique. TMJ, temporomandibular joint. 83 Furthermore, this trimodal integration can be extended to the diagnosis and treatment of temporomandibular joint (TMJ) disorders: combining mandibular motion parameters with imaging data such as cone-beam computed tomography (CBCT) enables the construction of a dynamic condylar motion model, which accurately delineates the three-dimensional dynamic relationship between the condyle and the glenoid fossa ( Figure 6 A). 86 This forms a comprehensive three-dimensional dynamic model covering the dentition, osseous tissues, and joint structures ( Figure 6 B). 87 To verify the reliability of this model, Xu et al 88 completed the clinical validation of dynamic condylar position assessment using an in vitro dynamic simulation system, which compensates for the limitations of conventional static detection or subjective judgment. CBCT+ MRI+ Jaw motion tracking system While the two trimodal paradigms mentioned above have achieved the integration of hard tissues and motor functions, they still have limitations – insufficient capability in observing soft tissue structures. However, the dynamic state of soft tissues such as the articular disc serves as a critical diagnostic basis in the management of temporomandibular disorders (TMD). Against this backdrop, the trimodal combination of ‘cone-beam computed tomography (CBCT) + magnetic resonance imaging (MRI) + mandibular motion tracking’ has emerged, with its core objective being to achieve dynamic assessment through the synergistic observation of hard and soft tissues 89 ( Figure 6 C). However, the existing technical system still has notable limitations: first, the registration process is cumbersome and time-consuming; second, the image fusion based on static MRI of five jaw positions in Zhang’s experiment (ie, ‘pseudo-dynamic’ reconstruction) cannot truly replicate the real-time dynamic movement of the TMJ. Studies have shown that integrating dynamic MRI sequences into static MRI sequences significantly enhances the clinical efficacy of dynamic TMJ assessment. 90 In recent years, scholars have successively developed dynamic imaging technologies based on echo-planar MRI (EPI), half-Fourier single-shot fast spin-echo (HASTE) sequences, fast imaging with steady-state precession (FISP) sequences, and fast imaging employing steady-state acquisition (FIESTA), etc. 91 With future advancements in fast MRI sequence technology, integrating dynamic MRI and CBCT data is expected to enable accurate modelling of TMJ physiological movements. The fusion of four modalities: face scan + Intraoral scanning + CBCT + Virtual facebow/Jaw motion tracking system/Finite element analysis system As oral diagnosis and treatment advances toward the goals of precision, personalization, and interdisciplinary collaboration, trimodal integration has become insufficient to meet the demands of comprehensive diagnostic and therapeutic needs. Neither the basic trimodal integration focusing on hard tissue-movement correlation nor the advanced trimodal integration enabling synergistic observation of hard and soft tissues has achieved full coverage of ‘morphology-function-biomechanics’. Against this backdrop, the quadrimodal technology integrating ‘intraoral scanning + facial scanning + CBCT + virtual facebow/jaw motion tracking /finite element analysis’ has emerged. By adding new modalities to supplement the dimensions of aesthetic evaluation and biomechanical analysis, this technology constructs a comprehensive four-dimensional dynamic virtual patient model. Existing research has successfully applied function-oriented virtual patient modelling technology to three-dimensional implant planning and digital restoration design. This technology integrates facial scanning, intraoral scanning, and cone-beam computed tomography (CBCT) data, combined with the virtual facebow-articulator system ( Figure 7 A). 92 , 93 , 94 , 95 , 96 Leveraging anatomical reference systems such as the Frankfurt plane and nasolabial angle, together with soft tissue deformation prediction algorithms, this model enables quantitative evaluation of post-restoration aesthetic outcomes. 96 Fig. 7. Open in a new tab Applications of Four-Modal Data Fusion: A, Prosthetic articulator-based implant rehabilitation virtual patient. 94 B, The prototype of an integrated system for planning and assisting maxillofacial orthognathic surgery. (a) data acquisition, images segmentation and 3D models reconstruction. (b) Models examination, diagnosis and surgery planning. (c) Assistance during the surgery. 98 C, (a) Bite force distribution measured with T-Scan and calculated with FE model. (b) Mesh of the FE model for bite force analysis with the bite plate. (c)Arrows showing the muscle forces generated during opening movement. 99 On this basis, quadrimodal integration achieves in-depth expansion of functional and biomechanical dimensions by incorporating ‘mandibular motion tracking’ or ‘finite element analysis’. On the one hand, the integration of mandibular motion data further improves the dynamic functionality of the model – stable fusion of video tracking data and facial scans is realized through triangular mesh registration and transformation matrix calculation 97 Lszewski et al 98 innovatively incorporated MRI data to visualize critical structures such as the inferior alveolar nerve, providing anatomical navigation for precision surgery ( Figure 7 B). On the other hand, the introduction of finite element analysis (FEA) breaks through the biomechanical dimension: the computer-aided design (CAD)-based virtual occlusal contact analysis system simulates the distribution and transmission pathway of occlusal force, offering a biomechanical basis for occlusal reconstruction. Dai et al 99 integrated CBCT, digital models, and mandibular motion data to construct individualized finite element models of the masticatory system, which successfully simulated periodontal stress distribution under muscle force loading, providing support for the optimization of orthodontic treatment plans. In the future, with the development of high-resolution MRI technology, quadrimodal integration will be further refined to the ‘sub-tissue level’. By acquiring parameters such as the microstructures of the temporomandibular joint disc, the mechanical properties of the periodontal ligament, and the direction of muscle fibres, biomechanical models will advance from the organ level to the sub-tissue level. 99 , 101 Ultimately, this will provide comprehensive theoretical support and decision-making basis for interdisciplinary collaborative diagnosis and treatment in prosthodontics, implantology, orthodontics, and other fields. Discussion and conclusion Multimodal data fusion technology demonstrates significant clinical utility in oral medicine, with its core advantage lying in the construction of comprehensive analytical models that integrate multidimensional biomechanical parameters, including dental occlusion characteristics, osseous structural parameters, soft tissue morphological features, muscle group dynamic properties, and mandibular motion trajectories. This technological framework not only provides orthodontics with quantitative tools for dynamic treatment monitoring and outcome evaluation but also enhances interdisciplinary collaboration efficiency between orthodontists and specialists in maxillofacial surgery, prosthodontics, and related fields through the establishment of standardized cross-disciplinary data platforms. Moreover, an increasing number of researchers are leveraging multimodal data integration to construct full-featured virtual patient platforms, which enable integrated diagnostic analysis and targeted treatment planning. For example, artificial intelligence-based virtual patient models have been built, which incorporate intraoral scanning, facial scanning, CBCT, mandibular motion data, MRI data, T-Scan occlusal data and electromyographic data, to enable accurate and efficient occlusal reconstruction and restoration. 100 Additionally, researchers have developed OralGPT-Omni – the first dental-specific multimodal large language model (MLLM). 101 This model integrates diverse data modalities, including intraoral images, panoramic radiographs, periapical radiographs, cephalometric radiographs, histopathological images, interleaved image-text data for treatment planning, intraoral videos and 3D model scans. Leveraging multi-source data construction, the TRACE-CoT reasoning paradigm and a four-stage training strategy, OralGPT-Omni performs tasks such as inferential diagnosis. Meanwhile, the first unified dental multimodal benchmark MMOral-Uni has been proposed, which fills the research gap in the field of intelligent dental analysis. However, there are still several key technical bottlenecks in the current technical system: Firstly, limited by the spatial and temporal heterogeneity of multi-source heterogeneous data sets, the fusion accuracy of existing registration algorithms has not yet reached the ideal clinical standard. Secondly, the registration process still relies on clinical physicians for manual data correction, resulting in an increase in the complexity of the operation process and the rise of time costs. Even with the large language models of artificial intelligence, limitations still exist, such as reliance on external models, high training costs, and low diagnostic accuracy for rare cases. To address these limitations, future research should prioritize the following directions:1) Developing an adaptive registration algorithm based on deep learning to address the differences in spatial resolution among different imaging modalities; 2) Constructing a fully automatic data processing flow, and improving the data processing efficiency by optimizing the GPU parallel computing architecture; 3) Develop a four-dimensional (4D) virtual patient system incorporating micro-parameters such as the hydrodynamic characteristics of the temporomandibular joint (TMJ) and periodontal ligament stress distribution. It can even integrate multi-omics data including genomics and oral microbiome data, ultimately achieving accurate biomechanical simulation of the multi-tissue hierarchy in the oromaxillofacial region and a multi-dimensional integrated diagnostic model. 4) Optimize the existing reasoning paradigm, build a dynamic knowledge base that is real-time synchronized with clinical guidelines, iteratively refine the reasoning logic based on physician feedback, and improve its credibility. 5) Develop seamless docking interfaces with electronic medical record systems and dental diagnosis & treatment equipment, construct real-time auxiliary diagnostic workstations, and promote the standardized clinical application of the system. Author’s contribution Yuqian Feng contributed for literature collection, data curation and writing-original draft; Yifan Jia contributed for methodology and writing-editing; Linhe Lv contributed for figure editing; Min Hu contributed for supervision; Xiaoxi Wei contributed for project administration, conceptualization, writing-review & editing and funding acquisition. Funding Project supported by the National Natural Science Foundation of China (NSFC-82001083), Project of the Jilin Province Department of Finance (jcsz2025678-16) and Project of the Jilin Province Department of Science and Technology (YDZJ202501ZYTS080). Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The data supporting this study’s findings are available on request from the corresponding author. Contributor Information Xiaoxi Wei, Email: [email protected]. 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