Table_2_Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images.docx

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo 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.
MethodsA 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.
ResultsThe 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/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.
ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

**研究目的** 为实现活体共聚焦显微镜（in vivo confocal microscopy, IVCM）拍摄的角膜图像的自动化快速分层识别，并将其划分为正常与异常图像，本研究基于深度学习构建并验证了一款计算机辅助诊断模型，以减轻临床医师的工作负担。

**研究方法** 本研究从中国武汉武汉大学人民医院及武汉大学中南医院2021年1月至2022年8月期间接受IVCM检查的423例患者中，回顾性收集共计19612幅角膜图像。在训练及验证模型（包括角膜图像分层识别模型与诊断模型）前，由3名角膜专科医师对所有图像进行标注与分类，以实现角膜图像分层（上皮层、鲍曼膜、基质层及内皮层）识别以及正常/异常图像区分。此外，选取580幅独立于训练集的IVCM图像开展人机竞赛，评估4名眼科医师与人工智能（artificial intelligence, AI）的图像识别速度与准确率。为评估模型的辅助效能，招募8名培训医师分别在有无模型辅助的情况下识别该580幅图像，通过对比两次评估结果以分析模型辅助的作用效果。

**研究结果** 在内部测试集中，该模型对上皮层、鲍曼膜、基质层及内皮层四层角膜图像的识别准确率分别为0.914、0.957、0.967及0.950；对各分层对应的正常/异常图像的识别准确率分别为0.961、0.932、0.945及0.959。在外部测试集中，模型对角膜四层结构的识别准确率分别为0.960、0.965、0.966及0.964；对各分层对应正常/异常图像的识别准确率分别为0.983、0.972、0.940及0.982。在人机竞赛中，模型的识别准确率达0.929，与专科医师相当且高于高年资医师，其识别速度较专科医师快237倍。在模型辅助下，培训医师的识别准确率从0.712提升至0.886。

**研究结论** 本研究基于深度学习构建了一款针对IVCM角膜图像的计算机辅助诊断模型，可实现角膜图像的快速分层识别并将其划分为正常与异常类别。该模型可提升临床诊断效能，同时可辅助医师开展临床培训与学习。