Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Understanding the Relationship Between Germ Layer Origin and Cancer Therapy Response: A Systematic Review

Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malignancies exhibit exceptional responsiveness to cellular immunotherapy, endoderm-derived epithelial cancers demonstrate marked sensitivity to protein signaling inhibitors, and ectoderm-derived tumors show heightened immunogenicity enabling breakthrough...

Evaluating an LLM-Assisted Workflow for Clinical Documentation: A Pilot Randomized Controlled Trial on Time and Quality

Large language models (LLMs) have been investigated for clinical documentation, with concerns about hallucinations and factual errors. Clinician review and revision of LLM-generated drafts are therefore considered essential, yet the impact of such workflow on both documentation time and quality remains unknown. To assess whether physician review and editing of LLM-generated drafts improves the tim...

Recognizing “Conformity Bias” in Large Language Models: A New Risk for Clinical Use

The aim of the present study is to systematically investigate the phenomenon of Conformity Bias in contemporary LLMs, specifically evaluating how repe...

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their firs...

Mind’s eye: Saccade-related evoked potentials support visual encoding in humans

In active vision, the brain receives and encodes discontinuous streams of visual information gated by saccadic eye movements. Saccadic modulation of n...

A Bibliometric and Visual Analysis of Global Research Trends in the Immunogenomics of Leishmania spp. and Toxoplasma gondii within a One Health Framework

Leishmaniasis and toxoplasmosis are major neglected zoonotic diseases affecting billions globally, with diverse clinical outcomes driven by complex ho...

Development and Clinical Validation of Lightweight, Multimodal Machine Learning Models for Smartphone-Based Cataract Detection and Classification

Globally, cataract remains the leading cause of blindness, affecting over 100 million people, with a disproportionate burden in low- and middle-income...

3DeepVOG: An Open-Source Framework for Real-Time, Accurate 3D Gaze Tracking with Deep Learning

Eye movements are key biomarkers for diagnosing and monitoring neuro-otological, neuro-ophthalmological and neurodegenerative disorders. Video-oculogr...

Multimodal Deep Learning for Longitudinal Prediction of Glaucoma Progression Using Sequential RNFL, Visual Field, and Clinical Data

Forecasting glaucoma progression remains a major challenge in preventing irreversible vision loss. We developed and validated a multimodal, longitudin...

Mobile Objective Diagnostics of Macular Degeneration using Dark-Adapted Visual Evoked Potentials

Delayed Dark-Adapted vision Recovery (DAR) is a known biomarker for Age-related Macular Degeneration (AMD); however, its measurement is often cumberso...

Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial Intelligence

Pregnancy orchestrates a rare physiological transformation across vascular, immune, and metabolic systems. When this dynamic balance is disrupted – as...

REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease

Deep learning foundation models excel at disease prediction from medical images, yet their potential to bridge tissue morphology with the genetic arch...

Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery

The genetic architecture of primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, remains largely unexplained due to the reli...

Machine Learning–based Prediction of LASIK Console Inputs for Aspheric Planning (Q-factor, Defocus, Astigmatism): A Translational Methods Study

Aspheric planning in laser refractive surgery remains difficult: surgeons often rely on empirical nomo-grams or simple linear regression for defocus a...

Operational Survival Deficit of Neoadjuvant Chemotherapy in Early-Stage Breast Cancer: A Target Trial Emulation and Causal Machine Learning Study

Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Large Language Model

To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...

A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs

Glaucoma, a leading cause of blindness worldwide, depends on accurate optic nerve head assessment, particularly optic disc and cup segmentation, for d...

Glaucoma Detection Using Deep Learning and Prompt-Based Explainable Report Generation

Glaucoma is a leading cause of irreversible blindness and requires early detection to prevent vision loss. This study proposes a novel framework for a...

The Silent Majority: Demystifying Memorization Effect in the Presence of Spurious Correlations

Machine learning models often rely on simple spurious features -- patterns in training data that correlate with targets but are not causally related...

2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining

Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. Howev...

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