AUTHOR=Li Zhongwen , Wang Lei , Qiang Wei , Chen Kuan , Wang Zhouqian , Zhang Yi , Xie He , Wu Shanjun , Jiang Jiewei , Chen Wei TITLE=DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones JOURNAL=Frontiers in Cell and Developmental Biology VOLUME=Volume 12 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2024.1447067 DOI=10.3389/fcell.2024.1447067 ISSN=2296-634X ABSTRACT=

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.