| Autor: |
Jing-hao Qu, Xiao-ran Qin, Zi-jun Xie, Jia-he Qian, Yang Zhang, Xiao-nan Sun, Yu-zhao Sun, Rong-mei Peng, Ge-ge Xiao, Jing Lin, Xiao-yan Bian, Tie-hong Chen, Yan Cheng, Shao-feng Gu, Hai-kun Wang, Jing Hong |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Big Data, Vol 11, Iss 1, Pp 1-20 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2196-1115 |
| DOI: |
10.1186/s40537-024-00913-w |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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