AI Determine Malignancy of GGO on Chest CT

Research Objectives To use AI computer-aided detection software to assist physicians in reading CT scans of lung nodules, providing auxiliary diagnostic tools for medical decision-making. The software can mark nodule locations and related information during routine physician reading. This study will obtain prospective consent to use patient CT images for software reading and compare with clinical physician diagnosis, in order to enhance software training and improve recognition of lung lesions for early diagnosis and treatment. Study Design Collect CT images of untreated lung nodules 4-30mm in size that are scheduled for surgery. No limits on age, gender, disease type, with image resolution \<2.5mm. AI and clinicians will judge nodule characteristics separately. Surgical resection followed by comparison with pathology reports will evaluate diagnostic accuracy. Study Procedures A double-blinded method will be used. AI and physicians will record nodules as likely benign or malignant separately. After surgical resection, the lesions will undergo pathological staging and the diagnostic accuracy of both groups will be compared. Expected Results Compare the diagnostic accuracy of AI and clinicians to improve AI training quality, achieve early diagnosis and treatment goals, and provide patients with better medical care quality. Monitoring Method AI and clinicians will read separately, adhering to shared decision making without affecting patient access to diagnosis and treatment. Keywords: lung nodules, early lung cancer, artificial intelligence, chest CT, minimally invasive surgery, lung image analysis software