| Autor: |
Zhongwen Li, Lei Wang, Wei Qiang, Kuan Chen, Zhouqian Wang, Yi Zhang, He Xie, Shanjun Wu, Jiewei Jiang, Wei Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Cell and Developmental Biology, Vol 12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-634X |
| DOI: |
10.3389/fcell.2024.1447067 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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