一种基于人工智能和远程医疗的筛查工具，用于从彩色眼底图像中识别青光眼疑似患者。
An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging.
作者信息
Bhuiyan Alauddin, Govindaiah Arun, Smith R Theodore
机构信息
iHealthscreen Inc., New York, NY, USA.
New York Eye and Ear Infirmary, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
出版信息
J Ophthalmol. 2021 May 28;2021:6694784. doi: 10.1155/2021/6694784. eCollection 2021.
RESULTS
The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85).
CONCLUSIONS
Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.
结果
该系统的准确率为89.67%（灵敏度为83.33%；特异度为93.89%；曲线下面积为0.93）。为进行外部验证，使用了用于青光眼分析的视网膜眼底图像数据库数据集，该数据集有638张可分级质量的图像。在此，该模型的准确率为83.54%（灵敏度为80.11%；特异度为84.96%；曲线下面积为0.85）。
结论
我们已展示了一种可部署在远程医疗平台上的准确且全自动的青光眼疑似病例筛查系统，我们计划开展前瞻性试验以确定该系统在初级保健环境中的可行性。
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