Development of Deep Learning Models to Screen Posterior Staphylomas in Highly Myopic Eyes Using UWF-OCT Images.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-06-02 DOI:10.1167/tvst.14.6.25

Yining Wang, Changyu Chen, Ziye Wang, Yijin Wu, Hongshuang Lu, Jianping Xiong, Keigo Sugisawa, Koju Kamoi, Kyoko Ohno-Matsui

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引用次数: 0

Abstract

Purpose: To develop a deep learning (DL) model for screening posterior staphylomas in highly myopic patients using ultra-widefield optical coherence tomography (UWF-OCT) images.

Methods: Our retrospective single-center study collected 1428 qualified UWF-OCT images from 438 highly myopic patients between 2017 and 2019 for model development. An independent test dataset for internal validation included 216 images from 69 highly myopic patients obtained between June 2020 and December 2020. Posterior staphylomas were detected by identifying the staphyloma edges. Seven independent architectures (VGG16, VGG19, ResNet18, ResNet50, ResNet101, DenseNet121, and DenseNet161) were used to train the models and identify staphyloma edges. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate and compare the performance of each model.

Results: The AUCs of seven DL models ranged from 0.794 (95% confidence interval [CI], 0.708-0.875) to 0.903 (95% CI, 0.846-0.953) for staphyloma edge detection in the internal test dataset. VGG19, with the highest AUC, achieved sensitivity (0.871; 95% CI, 0.773-0.931) that was comparable to or better than those of retina specialists. Heatmaps showed that the DL models could precisely identify the region of staphyloma edges.

Conclusions: Our models reliably identified staphyloma edges with high sensitivity and specificity. Given that posterior staphylomas are a key contributor to various fundus complications, the development of DL models holds significant promise for improving the clinical management of highly myopic patients.

Translational relevance: This effective artificial intelligence system can help ophthalmologists screen posterior staphylomas in highly myopic eyes.

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利用UWF-OCT图像筛选高度近视眼后葡萄肿的深度学习模型的发展。

目的：建立深度学习（DL）模型，应用超宽视场光学相干断层扫描（UWF-OCT）图像筛查高度近视患者的后葡萄肿。方法：我们的回顾性单中心研究收集了2017年至2019年438名高度近视患者的1428张合格的UWF-OCT图像，用于模型开发。用于内部验证的独立测试数据集包括2020年6月至2020年12月期间获得的69名高度近视患者的216张图像。通过确定葡萄瘤边缘来检测后葡萄瘤。使用VGG16、VGG19、ResNet18、ResNet50、ResNet101、DenseNet121和DenseNet161 7个独立的体系结构进行模型训练和葡萄肿边缘识别。采用受试者工作特征（ROC）曲线下面积（AUC）来评价和比较各模型的性能。结果：在内部测试数据集中，7个DL模型的葡萄肿边缘检测auc范围为0.794（95%置信区间[CI]， 0.708-0.875）至0.903 （95% CI, 0.846-0.953）。AUC最高的VGG19达到了灵敏度(0.871；95% CI, 0.773-0.931)，与视网膜专家相当或更好。热图显示DL模型能准确识别葡萄肿边缘区域。结论：我们的模型具有较高的敏感性和特异性，能够可靠地识别葡萄肿边缘。鉴于后葡萄肿是各种眼底并发症的关键因素，DL模型的发展对改善高度近视患者的临床管理具有重要意义。翻译相关性：这种有效的人工智能系统可以帮助眼科医生筛查高度近视眼的后葡萄肿。

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.