The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.

全世界角膜失明的主要原因是角膜炎，尤其是由细菌、真菌、病毒和棘阿米巴引起的感染形式。有效治疗感染性角膜炎的关键在于及时、准确的诊断。然而，当前的黄金标准，例如角膜刮片培养，仍然耗时且经常产生假阴性结果。在这里，这项研究利用从全国 12 个临床中心收集的 23,055 张裂隙灯图像，构建了一个临床上可行的深度学习系统 DeepIK，该系统可以模拟人类专家的诊断过程，以识别和区分细菌、真菌、病毒、阿米巴和非感染性角膜炎。 DeepIK 在内部、外部和前瞻性数据集（接收器工作特性曲线下的所有区域 > 0.96）中表现出卓越的性能，并且优于其他三种最先进的算法（DenseNet121、InceptionResNetV2 和 Swin-Transformer）。我们的研究表明，DeepIK能够帮助眼科医生从裂隙灯图像中准确、快速地识别各种感染性角膜炎类型，从而有助于及时、有针对性的治疗。