Ophthalmology

Delineating retinal breaks in ultra-widefield fundus images with a PraNet-based machine learning model

Retinal breaks are critical lesions that can lead to retinal detachment and vision loss if not detected and treated early. Automated and precise delineation of retinal breaks using ultra- widefield fundus (UWF) images remain a significant challenge in ophthalmology. This study aimed to develop and validate a deep learning model based on the PraNet architecture for the accurate delineation of retin...

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs

To compare the performance of a foundation model and a supervised learning-based model for detecting referable glaucoma from fundus photographs. Evaluation of diagnostic technology. 6,116 participants from the Los Angeles County Department of Health Services Teleretinal Screening Program. Fundus photographs were labeled for referable glaucoma (cup-to-disc ratio ≥ 0.6) by certified optometrists. Fo...

A hybrid computer vision model to predict lung cancer in diverse populations

Disparities of lung cancer incidence exist in Black populations and screening criteria underserve Black populations due to disparately elevated risk i...

Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging

To develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on B□scan ophthalmic ultraso...

Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease ...

Early Subtypes and Progressions of Progressive Supranuclear Palsy: A Data-Driven Brain Bank Study

Progressive supranuclear palsy (PSP) is typically characterized by vertical supranuclear gaze palsy and early falls, referred to as Richardson’s syndr...

Clinically Informed Semi-Supervised Learning Improves Disease Annotation and Equity from Electronic Health Records: A Glaucoma Case Study

Clinical notes represent a vast but underutilized source of information for disease characterization, whereas structured electronic health record (EHR...

Multimodal Machine Learning for Diagnosis of Multiple Sclerosis Using Optical Coherence Tomography in Pediatric Cases

Identifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic interven...

Open-source DeepSeek-R1 Outperforms Proprietary Non-Reasoning Large Language Models With and Without Retrieval-Augmented Generation

To compare reasoning large language models (LLMs) vs. non-reasoning LLMs and open-source DeepSeek models vs. proprietary LLMs in answering ophthalmolo...

Serious Gaming and Eye-Tracking for the Screening, Monitoring, Diagnosis and Treatment of Neurodevelopmental Disorders in Children: A Systematic Literature Review

Neurological development between the ages of 3 to 11 is crucial to the shaping of infrastructural capabilities like the executive functions that enabl...

An Artificial Intelligence Approach to Augmentative and Assistive Communication for Patients with Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) progressively impairs motor functions, making communication increasingly difficult for affected individuals. Howev...

A Deep Lightweight Convolutional Neural Network for Detecting Artifacts in Continuous EEG Signals

This study aimed to develop and validate a system of specialized deep lightweight convolutional neural networks (CNN) to accurately detect specific ar...

Pre-trained Vision Transformer With Masked Autoencoder for Automated Diabetic Macular Edema Detection from Optical Coherence Tomography Images

To develop and evaluate a novel self-supervised learning approach using Masked Autoencoder (MAE) pre-trained Vision Transformer (ViT) for automated de...

RetFit: A Novel Deep Learning Biomarker based on Cardiorespiratory Fitness derived from the Retina

Cardiorespiratory fitness (CRF) is a powerful predictor of cardiovascular events and overall mortality, often surpassing traditional risk factors in p...

Artificial Intelligence Powered Research Automation (AIPRA) Versus Human Expert: A Two-Arm Ophthalmology Comparative Study

To compare the quality and efficiency of an AI-powered research automation (AIPRA) workflow with a conventional human-led workflow for producing a ful...

Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures

Retinal fundus images offer a non-invasive window into systemic aging. Here, we fine-tuned a foundation model (RETFound) to predict chronological age ...

Explainable Deep Learning for Glaucoma Detection: A DenseNet121-Based Classification with Grad-CAM Visualization

One of the main causes of permanent blindness in the globe, glaucoma frequently advances symptomlessly until it reaches an advanced stage. Recent deve...

Classifying the severity of diabetic macular oedema from optical coherence tomography scans using deep learning: a feasibility study

Diabetic macular oedema (DME) is a vision-threatening complication of diabetes mellitus. It is reliably detected using optical coherence tomography (O...

Explainable Deep Learning for Lesion-Level Detection of Diabetic Retinopathy: A Segmentation Approach Using Fundus Images Graded as Mild-to-Moderate Nonproliferative Diabetic Retinopathy

Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...

Multimodal Prediction of Primary Open-Angle Glaucoma Using Polygenic Risk Scores and Clinical Features in a High-Risk African Ancestry Cohort

Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet early detection tools remain limited. Using the lar...