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中华眼科医学杂志(电子版) ›› 2024, Vol. 14 ›› Issue (01) : 57 -61. doi: 10.3877/cma.j.issn.2095-2007.2024.01.010

综述

人工智能在糖尿病视网膜病变中的应用进展
董力1, 李赫妍1, 魏文斌1,()   
  1. 1. 100730 首都医科大学附属北京同仁医院 北京同仁眼科中心 医学人工智能研究与验证工信部重点实验室 眼内肿瘤诊治研究北京市重点实验室 北京市眼科学与视觉科学重点实验室
  • 收稿日期:2023-10-25 出版日期:2024-02-28
  • 通信作者: 魏文斌
  • 基金资助:
    首都卫生发展科研专项(首发2024-1-2052); 国家自然科学基金(82220108017,82141128); 北京市科委科技计划项目(Z201100005520045); 深圳市"医疗卫生三名工程"项目(SZSM202311018)

Advances on the application progress of artificial intelligence in diabetic retinopathy

Li Dong1, Heyan Li1, Wenbin Wei1,()   

  1. 1. Beijing Tongren Eye Center, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
  • Received:2023-10-25 Published:2024-02-28
  • Corresponding author: Wenbin Wei
引用本文:

董力, 李赫妍, 魏文斌. 人工智能在糖尿病视网膜病变中的应用进展[J]. 中华眼科医学杂志(电子版), 2024, 14(01): 57-61.

Li Dong, Heyan Li, Wenbin Wei. Advances on the application progress of artificial intelligence in diabetic retinopathy[J]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2024, 14(01): 57-61.

糖尿病视网膜病变(DR)是糖尿病常见的微血管并发症,是中青年人群失明的主要原因,但我国仅有不到一半的糖尿病患者接受过眼底筛查。近年来,人工智能在DR领域的研究日益增多,以深度学习为代表的人工智能算法可通过对眼底照片和光学相干断层扫描成像进行图像识别,用于DR筛查和分级,可降低DR筛查成本,减轻眼科医师的工作负担。本文中笔者将从DR风险预测、DR筛查及DR分级等方面对人工智能在DR中的应用进行综述,旨在分析人工智能在DR诊疗领域的研究现状、困境及发展方向。

Diabetic retinopathy (DR), as a common microvascular complication of diabetes, has become the main cause of blindness in young and middle age adults. However, the fundus screening for less than half of diabetes patients in China has performed. In recent years, researches on the artificial intelligence in the DR application field have increase gradually. Some artificial intelligence algorithms represented by deep learning through fundus photos, optical coherence tomography and other images recognition have revealed their advantages in the screening and grading of DR, which reduces the screening cost and lightens the burden of ophthalmologists. The application of artificial intelligence in DR from the aspects of risk prediction, screening and grading were reviewed in this paper, aiming to analyze the research situation, difficulties and development direction of artificial intelligence in the diagnosis and treatment of DR.

表1 用于DR筛查的AI系统临床研究汇总
第一作者 年份 国家 人数 数据集 自主开发模型 模型 灵敏度(95%CI) 特异度(95%CI)
训练集 验证集 外部验证集
Gulshan等[21] 2016 美国 75 444 EyePACS(128 175例) EyePACS-1(9963例) Inception-V3 90.1(87.2~92.6) 98.2(97.8~98.5)
          Messidor-2(1748例)       90.7(89.2~92.1) 93.8(93.2~94.4)
Daniel等[22] 2017 新加坡 27 979 SIDRP(76 370例) SIDRP(71 896例) 10 VGGNet 90.5(87.3~93.0) 91.6(91.0~92.2)
Gargeya等[23] 2017 美国 EyePACS(75 137例) 15 000例 2 CNN 94.0 98.0
Sedova等[24] 2022 奥地利 54 IDx-DR 100.0 47.0
Bhaskaranand等[25]* 2019 美国 101 710 EyePACS(综合医院850 908例) EyeArt 91.3(90.9~91.7) 91.1(90.9~91.3)
Zhang等[26] 2020 中国 47 269 (143 626例)公开数据库(1184例) VoxelCloud Retina 83.3(81.9~84.6) 92.5(92.1~92.9)
Hacisoftaoglu等[29] 2020 美国 UoA-DR(200例) ResNet50 98.2 99.1
Sosale等[30] 2019 印度 900 EyePACS(34 278例)
Remidio FOP(4350例)
Medios AI 93.0(91.3~94.7) 92.5(90.8~94.2)
Abràmoff等[31] 2018 美国 819 基层医院(1638例) IDx-DR 87.2(81.8~91.2) 90.7(88.3~92.7)
Raumviboonsuk等[32] 2019 泰国 7517 国家筛查(25 326例) Inception-V4 96.8(89.3~99.3) 95.6(98.3~98.7)
Verbraak等[33]* 2019 荷兰 1293 综合医院(2586例) IDx-DR 79.4(66.5~87.9) 93.8(92.1~94.9)
He等[34]* 2019 中国 889 基层医院(3556例) Inception-V4 91.2(86.4~94.7) 98.8(98.1~99.3)
Kanagasingam等[35] 2018 澳大利亚 192 DiaRetDBI、EyePACS(30 000例) Inception-V3 100.0(19.8~100.0) 92.1(87.1~95.4)
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