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- Title
Establishment of an automatic diagnosis system for corneal endothelium diseases using artificial intelligence.
- Authors
Qu, Jing-hao; Qin, Xiao-ran; Xie, Zi-jun; Qian, Jia-he; Zhang, Yang; Sun, Xiao-nan; Sun, Yu-zhao; Peng, Rong-mei; Xiao, Ge-ge; Lin, Jing; Bian, Xiao-yan; Chen, Tie-hong; Cheng, Yan; Gu, Shao-feng; Wang, Hai-kun; Hong, Jing
- Abstract
Purpose: To use artificial intelligence to establish an automatic diagnosis system for corneal endothelium diseases (CEDs). Methods: We develop an automatic system for detecting multiple common CEDs involving an enhanced compact convolutional transformer (ECCT). Specifically, we introduce a cross-head relative position encoding scheme into a standard self-attention module to capture contextual information among different regions and employ a token-attention feed-forward network to place greater focus on valuable abnormal regions. Results: A total of 2723 images from CED patients are used to train our system. It achieves an accuracy of 89.53%, and the area under the receiver operating characteristic curve (AUC) is 0.958 (95% CI 0.943–0.971) on images from multiple centres. Conclusions: Our system is the first artificial intelligence-based system for diagnosing CEDs worldwide. Images can be uploaded to a specified website, and automatic diagnoses can be obtained; this system can be particularly helpful under pandemic conditions, such as those seen during the recent COVID-19 pandemic.
- Subjects
ENDOTHELIUM diseases; ARTIFICIAL intelligence; RECEIVER operating characteristic curves; CORNEA; COVID-19 pandemic; ENDOTHELIUM
- Publication
Journal of Big Data, 2024, Vol 11, Issue 1, p1
- ISSN
2196-1115
- Publication type
Article
- DOI
10.1186/s40537-024-00913-w