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人工智能在角膜相关疾病领域的应用研究

狄宇 李莹

狄宇, 李莹. 人工智能在角膜相关疾病领域的应用研究[J]. 协和医学杂志, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098
引用本文: 狄宇, 李莹. 人工智能在角膜相关疾病领域的应用研究[J]. 协和医学杂志, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098
DI Yu, LI Ying. The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098
Citation: DI Yu, LI Ying. The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098

人工智能在角膜相关疾病领域的应用研究

doi: 10.12290/xhyxzz.2020-0098
基金项目: 

白求恩·干眼诊疗与研究科研项目 BJ-GY2021015J

详细信息
    通讯作者:

    李莹  电话:010-69152733,E-mail: liyingpumch@126.com

  • 中图分类号: R770.4; TP18

The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases

Funds: 

Bethune·Dry eye Diagnosis and Treatment Research Project BJ-GY2021015J

More Information
  • 摘要: 人工智能(artificial intelligence,AI)是计算机领域的前沿科学,近年来在众多领域发展迅猛,其在眼科的研究和应用也日益增多。AI在角膜相关疾病领域的研究主要包括圆锥角膜的早期诊断及分级、角膜屈光手术相关评估、感染性角膜炎的分类及程度判断、角膜移植术后再干预的评估等,主要采用的算法包括神经网络、支持向量机及决策树,模型的灵敏度和特异度均达90%以上。AI可为医生提供客观的临床决策、为患者提供精准的治疗奠定基础。本文将对近年来AI在角膜相关疾病领域的应用研究进行综述。
    作者贡献:狄宇负责查阅文献、撰写论文;李莹负责指导论文写作方向、审阅及修订论文。
    利益冲突:
  • 表  1  圆锥角膜相关AI研究

    年份(年) 研究者 图像采集仪器 样本量(n) 分组情况 输入参数 AI算法 评价指标
    AUC 灵敏度 特异度
    2020 Kuo[16] TMS-4 326 圆锥角膜组,正常对照组 / VGG16 0.931 0.917 0.944
    Inception 0.931 0.917 0.944
    V3 0.958 0.944 0.972
    2019 Kamiya[17] CASIA 304 Ⅰ~Ⅳ级圆锥角膜组,正常对照组 / ResNet152 / 1.000 0.984
    2019 Lavric[18] Pentacam 1350 圆锥角膜组,正常对照组 / CNN 0.993 / /
    2019 Issarti[19] Pentacam 838 中重度圆锥角膜组,可疑圆锥角膜组,正常对照组 / FNN 0.966 0.956 0.978
    2018 Yousefi[20] CASIA 3156 Ⅰ~Ⅳ级圆锥角膜组,正常对照组 420个 非监督ML / 0.977 0.941
    2017 Hidalgo[21] Pentacam 135 圆锥角膜组,角膜屈光术后组,正常对照组 22个 CNN 0.989 0.991 0.985
    2016 Hidalgo[22] Pentacam 860 圆锥角膜组,顿挫型圆锥角膜组,正常对照组 25个 SVM 0.989 0.991 /
    2016 Kovács[23] Pentacam 135 双侧圆锥角膜组,单侧圆锥角膜组,正常对照组 15个 MLPNN 0.99 0.901.00 0.900.95
    2013 Smadja[24] Gailei 372 圆锥角膜组,顿挫型圆锥角膜组,正常对照组 55个 决策树 / 0.995 1.00
    2012 Arbelaez[25] Sirius 3502 圆锥角膜组,顿挫型圆锥角膜组,角膜屈光术后组,正常对照组 7个 SVM 0.982 0.95 0.993
    2010 Souza[26] OrbscanⅡ 318 圆锥角膜组,角膜屈光术后组,正常对照组 / SVM 0.99 1.00 1.00
    MLPNN 0.99 1.00 1.00
    RBFNN 0.99 0.98 0.98
    2005 Twa[27] Keratron 244 圆锥角膜组,正常对照组 / 决策树 0.93 0.93 0.92
    2002 Accardo[28] EyeSys 396 圆锥角膜组,正常对照组 9个 CNN 0.967 0.976 0.941
    1997 Smolek[29] TMS-1 300 圆锥角膜组,可疑圆锥角膜组 10个 CNN 1.00 1.00 1.00
    AI:人工智能;AUC:曲线下面积;CNN:卷积神经网络;FNN:前馈神经网络;ML:机器学习;SVM:支持向量机;MLPNN:多层感知器神经网络;RBFNN:径向基函数神经网络
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-27
  • 录用日期:  2021-01-11
  • 网络出版日期:  2021-09-01
  • 刊出日期:  2021-10-12

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