Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

作者：Yulin Yan, Weiyan Jiang, Yiwen Zhou, Yi Yu, Linying Huang, Shanshan Wan, Hongmei Zheng, Miao Tian, Huiling Wu, Li Huang, Lianlian Wu, Simin Cheng, Yuelan Gao, Jiewen Mao, Yujin Wang, Yuyu Cong, Qian Deng, Xiaoshuo Shi, Zixian Yang, Qingmei Miao, Biqing Zheng, Yujing Wang, Yanning Yang · 发表于：Frontiers in Medicine · 年份：2023 · DOI：10.3389/fmed.2023.1164188 · 被引用次数：10 · 研究领域：Corneal surgery and disorders、Ophthalmology and Visual Impairment Studies、Retinal Imaging and Analysis

Objective: confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload. Methods: A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance. Results: The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnorma...