AIMC Topic: Retinal Telangiectasis

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Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading.

Ophthalmology Sep 20, 2023
PURPOSE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed ...
Diabetic Retinopathy Disease Progression Humans Retinal Telangiectasis Deep Learning Fluorescein Angiography Tomography, Optical Coherence
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Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.

The British journal of ophthalmology Nov 23, 2020
AIM: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).
Tomography, Optical Coherence Retina Clinical Trials, Phase II as Topic Retinal Telangiectasis Humans Multicenter Studies as Topic Deep Learning
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Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.

Ophthalmology Dec 23, 2019
PURPOSE: To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).
Deep Learning Visual Acuity Treatment Outcome Reproducibility of Results Retinal Photoreceptor Cell Inner Segment Male Drug Implants Retinal Photoreceptor Cell Outer Segment Female Ciliary Neurotrophic Factor Humans Visual Fields Fluorescein Angiography Retinal Telangiectasis Visual Field Tests Tomography, Optical Coherence Retinal Vessels
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Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.

JAMA network open Feb 1, 2019
IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.
Deep Learning Tomography, Optical Coherence Middle Aged Aged Retinal Telangiectasis Algorithms Male Female Aged, 80 and over Humans Retina Image Interpretation, Computer-Assisted
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Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

JAMA ophthalmology Mar 1, 2024
IMPORTANCE: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.
Rare Diseases Humans Male Female Deep Learning Middle Aged Retinal Telangiectasis Supervised Machine Learning Retrospective Studies Tomography, Optical Coherence
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