AI Medical Compendium Journal:
Translational vision science & technology

Showing 61 to 70 of 208 articles

PhacoTrainer: Automatic Artificial Intelligence-Generated Performance Ratings for Cataract Surgery.

Translational vision science & technology
PURPOSE: To investigate whether cataract surgical skill performance metrics automatically generated by artificial intelligence (AI) models can differentiate between trainee and faculty surgeons and the correlation between AI metrics and expert-rated ...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

Translational vision science & technology
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
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...

An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

Translational vision science & technology
PURPOSE: To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide...

Automated Detection of Retinal Detachment Using Deep Learning-Based Segmentation on Ocular Ultrasonography Images.

Translational vision science & technology
PURPOSE: This study aims to develop an automated pipeline to detect retinal detachment from B-scan ocular ultrasonography (USG) images by using deep learning-based segmentation.

New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.

Translational vision science & technology
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.

Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

Translational vision science & technology
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...

RetOCTNet: Deep Learning-Based Segmentation of OCT Images Following Retinal Ganglion Cell Injury.

Translational vision science & technology
PURPOSE: We present RetOCTNet, a deep learning tool to segment the retinal nerve fiber layer (RNFL) and total retinal thickness automatically from optical coherence tomography (OCT) scans in rats following retinal ganglion cell (RGC) injury.

Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop an artificial intelligence (AI)-based intraocular lens (IOLs) power calculation formula for improving the accuracy of IOLs power calculations in patients with congenital ectopia lentis (CEL).

Artificial Intelligence for Optical Coherence Tomography in Glaucoma.

Translational vision science & technology
PURPOSE: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of ...