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首页> 外文期刊>JMIR Medical Informatics >A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study
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A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

机译：一种用于检测治疗视网膜血管疾病的多模式成像的深度学习模型：模型开发和验证研究

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Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.
机译：背景技术视网膜血管疾病，包括糖尿病黄斑水肿（DME），新生血管年龄相关性黄斑（NamD），近视脉络膜新生血管（MCNV）和分支和中央视网膜静脉闭塞（BRVO / CRVO）被认为是威胁视力的眼病。然而，准确的诊断取决于多峰影像和视网膜眼科医生的专业知识。目的本研究的目的是制定深入学习模型，以检测使用多式联运成像的视网膜血管疾病的治疗模型。方法采用2013年至2013年从台湾3家医院的3家医院注册了参与者的参与者参与了来自台湾的3家医院的参与者。使用眼睛相关的图像，包括通过视网膜眼底，光学相干断层扫描（OCT）和荧光素血管造影，或没有吲哚菁获得的图像绿色血管造影（FA / ICGA）。为检测DME，NAMD，MCNV，BRVO和CRVO构建了深度学习模型，并鉴定需要疾病。评估模型性能，并作为每个接收器操作特性曲线的曲线（AUC）下的区域。结果研究了2185名患者的2992只眼睛，分别在对照，DME，NamD，MCNV，BRVO和CRVO组中进行了2185名患者的2185名患者。其中，1898只眼睛需要治疗。眼睛分为5：1：1的训练，验证和测试组。总共有5117个视网膜眼底照片，9316 OCT图像和20,922个FA / ICGA图像。用于检测MCNV，DME，NAMD，BRVO和CRVO的AUC分别为0.996,0.995,0.990,0.959和0.988。检测需要疾病的AUC为0.969。从热图中，我们观察到该模型可以识别视网膜血管疾病。结论我们的研究开发了一种深入的学习模型，可使用多式联运眼科成像来检测视网膜疾病。此外，该模型在检测需要视网膜疾病方面表现出良好的性能。

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