Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.

IF 2.7 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2026-07-01 DOI:10.1167/tvst.15.7.3

Homa Rashidisabet, Jennifer I Lim, Andrius Kazlauskas, Darvin Yi

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引用次数: 0

Abstract

Purpose: The purpose of this study was to evaluate whether explicitly modeling diabetes mellitus (DM) without diabetic retinopathy (DR) as its own stage enables deep learning (DL) to detect early retinal changes for early risk identification of DR severity spectrum.

Methods: We developed 3 DL classification models that explicitly incorporated DM without DR as a distinct stage using 3-class, 4-class, and 6-class staging granularity using 6069 color fundus images from the University of Illinois Chicago Hospital, including 1996 no-DM cases, 1852 DM without DR cases, and 2221 DR cases (516 mild, 220 moderate, 103 severe, and 1382 proliferative DR [PDR]). We developed segmentation models for the optic nerve head (ONH) and retinal vessels to quantify the impact of these regions on classification performance through targeted perturbations. We also examined spatial changes in retinal features across DR stages by measuring the alignment between DL saliency maps and ONH location.

Results: For the 3-class model, areas under the curve (AUCs) were 92.2% (no-DM), 80.3% (DM without DR), and 74.1% (mild DR). For the 4-class model, AUCs were 94.0% (no-DM), 71.9% (DM without DR), 61.5% (mild DR), and 80.3% (referable DR). For the 6-class model, AUCs were 94.0% (no-DM), 65.7% (DM without DR), 65.6% (mild DR), 58.9% (moderate DR), 58.6% (severe DR), and 76.1% (PDR). Vessel perturbations reduced performance by 16% to 31% across models, and greater DR severity was associated with increased saliency-to-ONH distances (Pearson r = 0.69-0.72, P < 0.001).

Conclusions: Explicitly modeling diabetes without retinopathy improved early-stage discrimination and revealed feature-reliance shifts with DR severity. Vessel- and saliency-based analyses identified subtle retinal changes preceding clinical DR.

Translational relevance: Treating diabetes without retinopathy as its own stage may enhance early DR risk identification and aid development of clinically useful artificial intelligence (AI) tools.

查看原文本刊更多论文

糖尿病视网膜病变预测中明确纳入无视网膜病变糖尿病。

目的：本研究的目的是评估明确建模无糖尿病视网膜病变（DR）的糖尿病（DM）作为其自身阶段是否能够使深度学习（DL）检测早期视网膜变化，从而早期识别DR严重程度谱的风险。方法：采用伊利诺伊大学芝加哥医院6069张彩色眼底图像，采用3级、4级和6级的分级粒度，建立了3个DL分类模型，明确将无DR的DM作为一个不同的分期，其中包括1996例无DM、1852例无DR的DM和2221例DR（516例轻度、220例中度、103例重度和1382例增发性DR [PDR]）。我们开发了视神经头（ONH）和视网膜血管的分割模型，通过有针对性的扰动来量化这些区域对分类性能的影响。我们还通过测量DL显著性图和ONH位置之间的对齐来检查视网膜特征在DR阶段的空间变化。结果：3类模型的曲线下面积（aus）为92.2%（无糖尿病）、80.3%（无糖尿病）和74.1%（轻度糖尿病）。对于4级模型，auc分别为94.0%（无糖尿病）、71.9%（无糖尿病）、61.5%（轻度糖尿病）和80.3%（可参考糖尿病）。对于6级模型，auc分别为94.0%（无DM）、65.7%（无DR）、65.6%（轻度DR）、58.9%（中度DR）、58.6%（重度DR）和76.1% （PDR）。血管扰动使所有模型的性能降低16%至31%，更严重的DR与显著性到onh的距离增加相关（Pearson r = 0.69-0.72, P < 0.001）。结论：明确建模无视网膜病变的糖尿病改善了早期识别，并揭示了随DR严重程度的特征依赖变化。基于血管和显著性的分析发现了临床DR之前的细微视网膜变化。转化相关性：将无视网膜病变的糖尿病作为其自身阶段进行治疗，可以增强早期DR风险识别，并有助于临床有用的人工智能（AI）工具的开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.