Back to search

Article

Comprehensive Evaluation of Artificial Intelligence Models for Diagnosis of Multiple Sclerosis Using Information from Retinal Layers Multicenter OCT Images

2024-03-06

Abstract excerpt

Multiple sclerosis (MS) is a chronic inflammatory disease that affects the central nervous system. Optical coherence tomography (OCT) is a retinal imaging technology with great promise as a possible MS biomarker. Unlike other ophthalmologic diseases, the variations in shape of raw cross-sectional OCTs in MS are subtle and not differentiable from healthy controls (HCs). More detailed information like thickness of p...

Topics

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

Identifiers and source

Literature Corpus work
98d8d672-91e4-5d04-96ac-aaf0d7391878
DOI
10.1101/2024.03.05.24303789
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.
Comprehensive Evaluation of Artificial Intelligence Models for Diagnosis of Multiple Sclerosis Using Information from Retinal Layers Multicenter OCT ImagesDOI 10.1101/2024.03.05.24303789
SLO-MSNet: Discrimination of Multiple Sclerosis using Scanning Laser Ophthalmoscopy Images with Autoencoder-Based Feature ExtractionSemantic proximity
SLOTMFound: Foundation-Based Diagnosis of Multiple Sclerosis Using Retinal SLO Imaging and OCT Thickness-mapsSemantic proximity
Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseasesSemantic proximity
Deep Learning based Intraretinal Layer Segmentation using Cascaded Compressed U-NetSemantic proximity
Applications of Artificial Intelligence in Optometric Diagnostics: A Review of Techniques and Clinical ImpactSemantic proximity
Eye Disease Classification from OCT ImagesSemantic proximity
Artificial Intelligence for the Diagnosis of De Novo Diabetic Retinopathy: A Scoping Review of Global EvidenceSemantic proximity
Deriving OCT-Equivalent Retinal Nerve Fiber Layer Thickness Maps from Fundus Photographs with Deep Learning Improves Glaucoma DiagnosisSemantic proximity
An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and Self-supervised Deep Learning.Semantic proximity
Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkersSemantic proximity
Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkersSemantic proximity
Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scansSemantic proximity
Select a neighboring publication to make it the new centre.