Li Zhongwen, Wang Lei, Qiang Wei, Chen Kuan, Wang Zhouqian, Zhang Yi, Xie He, Wu Shanjun, Jiang Jiewei, Chen Wei
Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China.
National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
Front Cell Dev Biol. 2024 Aug 27;12:1447067. doi: 10.3389/fcell.2024.1447067. eCollection 2024.
Smartphone-based artificial intelligence (AI) diagnostic systems could assist high-risk patients to self-screen for corneal diseases (e.g., keratitis) instead of detecting them in traditional face-to-face medical practices, enabling the patients to proactively identify their own corneal diseases at an early stage. However, AI diagnostic systems have significantly diminished performance in low-quality images which are unavoidable in real-world environments (especially common in patient-recorded images) due to various factors, hindering the implementation of these systems in clinical practice. Here, we construct a deep learning-based image quality monitoring system (DeepMonitoring) not only to discern low-quality cornea images created by smartphones but also to identify the underlying factors contributing to the generation of such low-quality images, which can guide operators to acquire high-quality images in a timely manner. This system performs well across validation, internal, and external testing sets, with AUCs ranging from 0.984 to 0.999. DeepMonitoring holds the potential to filter out low-quality cornea images produced by smartphones, facilitating the application of smartphone-based AI diagnostic systems in real-world clinical settings, especially in the context of self-screening for corneal diseases.
基于智能手机的人工智能（AI）诊断系统可以帮助高危患者自行筛查角膜疾病（如角膜炎），而不是在传统的面对面医疗实践中进行检测，从而使患者能够在早期主动识别自己的角膜疾病。然而，由于各种因素，人工智能诊断系统在低质量图像中的性能显著下降，而在现实环境中（尤其是在患者记录的图像中很常见）低质量图像是不可避免的，这阻碍了这些系统在临床实践中的应用。在此，我们构建了一个基于深度学习的图像质量监测系统（深度监测），不仅用于识别智能手机生成的低质量角膜图像，还用于识别导致此类低质量图像生成的潜在因素，这可以指导操作人员及时获取高质量图像。该系统在验证集、内部测试集和外部测试集上表现良好，曲线下面积（AUC）范围为0.984至0.999。深度监测有潜力过滤掉智能手机生成的低质量角膜图像，促进基于智能手机的人工智能诊断系统在现实临床环境中的应用，特别是在角膜疾病自我筛查的背景下。
