■为了使用体内共聚焦显微镜(IVCM)自动,快速地识别角膜图像的各层,并将其分类为正常和异常图像,我们基于深度学习开发并测试了计算机辅助诊断模型,以减少医师的工作量.
■回顾性收集了武汉大学人民医院在2021年1月至2022年8月期间接受IVCM的423例患者的19,612张角膜图像(武汉,中国)和武汉大学中南医院(武汉,中国)。然后在训练和测试模型之前,由三名角膜专家对图像进行审查和分类。包括层识别模型(上皮,鲍曼的膜,基质,和内皮)和诊断模型,识别角膜图像的层次,区分正常图像和异常图像。完全正确,580张独立于数据库的IVCM图像用于人机竞赛,以评估4位眼科医生和人工智能(AI)的图像识别速度和准确性。为了评估模型的有效性,8名受训者被雇用来识别这580张图像,无论有没有模特帮助,并对两次评价的结果进行分析,以探讨模型辅助的效果。
■对于4层上皮的识别,模型的精度达到0.914、0.957、0.967和0.950,鲍曼的膜,基质,和内测数据集中的内皮,分别,每层正常/异常图像的识别分别为0.961、0.932、0.945和0.959,分别。在外部测试数据集中,角膜层的识别准确率分别为0.960,0.965,0.966,0.964,正常/异常图像识别的准确率分别为0.983、0.972、0.940和0.982。在人机竞争中,该模型的精度为0.929,与专家相似,高于高级医师,识别速度比专家快237倍。在模型的帮助下,学员的准确性从0.712提高到0.886。
■基于深度学习为IVCM图像开发了计算机辅助诊断模型,快速识别角膜图像层并将其分类为正常和异常。该模型可以提高临床诊断的功效,并帮助医生进行临床目的的培训和学习。
UNASSIGNED: In 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.
UNASSIGNED: 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.
UNASSIGNED: 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/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.
UNASSIGNED: A 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.