Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Ophthalmology and Therapy Pub Date : 2024-05-01 Epub Date: 2024-02-28 DOI:10.1007/s40123-024-00900-7

Xiaoling Wang, He Li, Hongmei Zheng, Gongpeng Sun, Wenyu Wang, Zuohuizi Yi, A'min Xu, Lu He, Haiyan Wang, Wei Jia, Zhiqing Li, Chang Li, Mang Ye, Bo Du, Changzheng Chen

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引用次数: 0

Abstract

Introduction: Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation.

Methods: This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists.

Results: We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation.

Conclusions: This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas.

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利用深度专家聚合技术通过超宽视场荧光素血管造影自动检测 30 种眼底疾病

前言眼底疾病诊断不准确、不及时会导致危及视力的并发症，甚至失明。我们建立了一个深度学习平台（DLP），利用超宽场荧光素血管造影术（UWFFA）和深度专家聚合技术自动检测 30 种眼底疾病：这项回顾性横断面数据库研究共纳入2016年至2021年的61609张UWFFA图像，涉及全国多个中心的3364名受试者。所有受试者被分为 30 个不同的组别。我们选择了最先进的卷积神经网络架构 ConvNeXt 作为骨干，在测试数据和外部测试日期上训练和测试所提系统的接收器工作特征曲线（ROC）。我们将拟建系统的分类性能与眼科专家（包括两名视网膜专家）的分类性能进行了比较：我们建立了一个DLP来分析UWFFA，它可以检测出多达30种眼底疾病，在主要测试数据集中的频率加权平均接收器工作特征曲线下面积（AUC）为0.940，在外部多医院测试数据集中的频率加权平均接收器工作特征曲线下面积（AUC）为0.954。在诊断和评估方面，该工具显示出与视网膜专科医生相当的准确性：这是首次针对大规模 UWFFA 数据集进行的多视网膜疾病分类研究。我们相信，我们的 UWFFA DLP 将推进人工智能（AI）对各种视网膜疾病的诊断，并将为节省人力和精准医疗做出贡献，尤其是在偏远地区。

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来源期刊

Ophthalmology and Therapy OPHTHALMOLOGY-

CiteScore

4.20

自引率

3.00%

发文量

157

审稿时长

6 weeks

期刊介绍： Aims and Scope Ophthalmology and Therapy is an international, open access, peer-reviewed (single-blind), and rapid publication journal. The scope of the journal is broad and will consider all scientifically sound research from preclinical, clinical (all phases), observational, real-world, and health outcomes research around the use of ophthalmological therapies, devices, and surgical techniques. The journal is of interest to a broad audience of pharmaceutical and healthcare professionals and publishes original research, reviews, case reports/series, trial protocols and short communications such as commentaries and editorials. Ophthalmology and Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. 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