深度学习辅助测量外层视网膜层指标作为遗传性视网膜退行性变的生物标志物：机遇与挑战。
Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges.
机构信息
Retina Foundation of the Southwest, Dallas, Texas.
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
出版信息
Curr Opin Ophthalmol. 2024 Nov 1;35(6):447-454. doi: 10.1097/ICU.0000000000001088. Epub 2024 Aug 28.
PURPOSE OF REVIEW
The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs.
RECENT FINDINGS
(clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes).
SUMMARY
Assessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.
目的综述
本文旨在总结目前用于评估遗传性视网膜退行性疾病（IRDs）的视网膜成像和视觉功能检测方法，重点介绍深度学习（DL）方法在辅助确定 IRD 结构生物标志物方面的应用。
最新发现
（IRDs 的临床试验；发现有效的生物标志物作为终点；DL 在处理视网膜图像以检测与疾病相关的结构变化方面的应用）。
总结
评估光感受器损失是评估 IRD 的直接方法。外视网膜层结构，包括外核层、椭圆体带、光感受器外节、RPE，是 IRD 的潜在结构生物标志物。可能需要进一步研究结构和功能关系。
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