Back to search

Article

Applications of Artificial Intelligence in Optometric Diagnostics: A Review of Techniques and Clinical Impact

2026-03-23

Abstract excerpt

This work presents deep learning–based models for automated detection of glaucoma, cataract, and diabetic retinopathy using ophthalmic images. Glaucoma detection utilizes retinal fundus images focusing on the optic disc and cup regions, along with optional optical coherence tomography (OCT) features, to classify normal and diseased cases and assess severity. Cataract detection is performed using slit-lamp and lens...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
90f80e78-cd9a-5a8c-8d0f-a39186974675
DOI
10.20944/preprints202603.1705.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Applications of Artificial Intelligence in Optometric Diagnostics: A Review of Techniques and Clinical ImpactDOI 10.20944/preprints202603.1705.v1
Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus ImagesSemantic proximity
Eye Disease Classification from OCT ImagesSemantic proximity
A Tutorial on Automated Classification of Eye Diseases Using Deep LearningSemantic proximity
A generalized computer vision model for improved glaucoma screening using fundus imagesSemantic proximity
Enhancing Glaucoma Detection through Supervised Pre-training with Intermediate Phenotypes: A Multi-Institutional StudySemantic proximity
Advanced Machine Learning Techniques for Retinal Lesion Segmentation: A Comprehensive Review Across Ophthalmic PathologiesSemantic proximity
Multi-task Deep Learning for Glaucoma Detection From Color Fundus ImagesSemantic proximity
EyeDx: A Scalable and Interpretable Multitask Deep Learning Framework for Comprehensive Ocular Disease Detection in Fundus ImagesSemantic proximity
EDNet-20: Enhancing Multi-Label Ocular Disease Detection through Deep Learning OptimizationSemantic proximity
Autonomous Screening for Laser Photocoagulation in Fundus Images Using Deep LearningSemantic proximity
Eye Disease Classification by Advanced Deep Transfer Learning System Using Resnet50 & XceptionSemantic proximity
Artificial Intelligence for the Diagnosis of De Novo Diabetic Retinopathy: A Scoping Review of Global EvidenceSemantic proximity
Select a neighboring publication to make it the new centre.