AIMC Topic: Tomography, Optical Coherence
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Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while mainta...
Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.
Journal of cataract and refractive surgery
May 1, 2025
PURPOSE: To develop a prediction model based on machine learning to calculate the postoperative vault and the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a White population.
Machine Learning-Based Immuno-Inflammatory Index Integrating Clinical Characteristics for Predicting Coronary Artery Plaque Rupture.
Immunity, inflammation and disease
Apr 1, 2025
BACKGROUND: Coronary artery plaque rupture (PR) is closely associated with immune-inflammatory responses. The systemic inflammatory index (SII) and the systemic inflammatory response index (SIRI) have shown potential in predicting the occurrence of P...
RETINAL IMAGING ANALYSIS PERFORMED BY CHATGPT-4o AND GEMINI ADVANCED: The Turning Point of the Revolution?
Retina (Philadelphia, Pa.)
Apr 1, 2025
PURPOSE: To assess the diagnostic capabilities of the most recent chatbots releases, GPT-4o and Gemini Advanced, facing different retinal diseases.
Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.
Translational vision science & technology
Mar 3, 2025
PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography ...
Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation.
Translational vision science & technology
Mar 3, 2025
PURPOSE: The purpose of this study was to develop a robust and general purpose artificial intelligence (AI) system that allows the identification of retinal optical coherence tomography (OCT) volumes with pathomorphological manifestations not present...
Structure-Function Correlation of Deep-Learning Quantified Ellipsoid Zone and Retinal Pigment Epithelium Loss and Microperimetry in Geographic Atrophy.
Investigative ophthalmology & visual science
Mar 3, 2025
PURPOSE: The purpose of this study was to define structure-function correlation of geographic atrophy (GA) on optical coherence tomography (OCT) and functional testing on microperimetry (MP) based on deep-learning (DL)-quantified spectral-domain OCT ...
Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.
Investigative ophthalmology & visual science
Mar 3, 2025
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...
CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.
Journal of cataract and refractive surgery
Mar 1, 2025
PURPOSE: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion.
New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.
Translational vision science & technology
Feb 3, 2025
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retinal data in the context of automatic diagnosis of early-stage multiple sclerosis (MS) with decision explanation.
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