Li Chun-Peng, Dai Weiwei, Xiao Yun-Peng, Qi Mengying, Zhang Ling-Xiao, Gao Lin, Zhang Fang-Lue, Lai Yu-Kun, Liu Chang, Lu Jing, Chen Fen, Chen Dan, Shi Shuai, Li Shaowei, Zeng Qingyan, Chen Yiqiang
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Sci Rep. 2024 Aug 8;14(1):18432. doi: 10.1038/s41598-024-68768-y.
Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis.
及时有效地诊断真菌性角膜炎（FK）对于为患者提供恰当治疗并避免不可逆的视力丧失至关重要。体内共焦显微镜检查（IVCM）已被广泛用于指导FK的诊断。我们提出了一种深度学习框架，利用IVCM图像诊断真菌性角膜炎，以协助眼科医生。受实际诊断过程的启发，我们的方法采用了一种两阶段深度架构，基于图像级和序列级信息进行诊断预测。据我们所知，我们收集了总共96,632张IVCM图像的最大数据集，并由专家进行标注，以训练和评估我们的方法。我们的方法在未见测试集上诊断FK的特异性和敏感性分别达到了96.65%和97.57%，与经验丰富的眼科医生相当或更好。该网络可以为医生提供图像级、序列级和患者级的诊断建议。结果显示出在协助眼科医生进行FK诊断方面的巨大潜力。
