DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones - Li, Zhongwen; Wang, Lei; Qiang, Wei; Chen, Kuan; Wang, Zhouqian; Zhang, Yi; Xie, He; Wu, Shanjun; Jiang, Jiewei; Chen, Wei (Frontiers in Cell and Developmental Biology, 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 lowquality images which are unavoidable in real-world environments (especially common in patientrecorded 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.