Latest AI and machine learning research in ophthalmology for healthcare professionals.
PURPOSE OF REVIEW: This review aims to highlight the expanding role of big data in ophthalmology, provide a comparison of the most prominent databases, and their use in glaucoma-specific research. Understanding the strengths and limitations of each database allows researchers to tailor their research questions appropriately. RECENT FINDINGS: Several large-scale databases have emerged in ophthalmol...
PURPOSE OF REVIEW: To highlight emerging applications of anterior segment optical coherence tomography (AS-OCT) in the diagnosis, risk stratification, and management of angle closure glaucoma, with particular emphasis on the integration of artificial intelligence. RECENT FINDINGS: AS-OCT enables objective and reproducible quantification of anterior chamber angle parameters, overcoming the subjecti...
Retinopathy of prematurity (ROP) is a vasoproliferative blinding disorder of the retina, unique to premature infants and a leading cause of preventabl...
OBJECTIVES: To introduce the KANET-connectome matrix (KANET-Con) as a conceptual framework linking fetal behaviors observed on four-dimensional (4D) u...
PURPOSE: To objectively quantify the motion paths of surgical instruments during cataract surgery across a resident's training, identifying patterns o...
Against the backdrop of China's new urbanization and regional coordination strategies, the social integration of rural-to-urban migrants has become an...
OBJECTIVE: To develop an interpretable machine learning (ML) model using routine blood parameters for high myopia (HM) screening as a convenient and c...
Ritchie and colleagues propose that the functional organization of higher visual cortex is best understood through the lens of behavioral relevance, a...
PURPOSE: Limited clinical data and unstable animal models hinder understanding isotretinoin-induced keratoconus. We addressed this by integrating phar...
PURPOSE: Observing choroidal volume changes in myopia with various degrees via spectral-domain optical coherence tomography (SD-OCT) images. METHODS: ...
PURPOSE: To develop and validate OCT-PRO, a multimodal machine learning model integrating OCT images and clinical traits to predict postoperative visu...
PURPOSE: To develop an integrated predictive model combining radiomics, clinical risk factors, and machine learning for prognostic assessment in hepat...
PURPOSE: To evaluate whether the difference between artificial intelligence (AI)-estimated retinal biological age and chronological age-the retinal ag...
Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on...
PURPOSE: To compare the performance of a vision transformer-based foundation model (RETFound) and a supervised convolutional neural network (VGG-19) f...
BACKGROUND: The American Academy of Ophthalmology recommendations on screening for hydroxychloroquine (HCQ) retinopathy are now a decade old. This rev...
PURPOSE: To assess the quality of Chat Generative Pre-Trained Transformer-4 Omni (ChatGPT-4o) responses to questions submitted by patients through Epi...
PURPOSE: To objectively identify subclinical keratoconus (SKC) from a large sample of healthy and keratoconus (KC) patients via a data-driven framewor...
PURPOSE: AlphaMissense is a newer deep learning-based variant predictor that evaluates the structural consequences of missense variants, the most comm...