| Autor: |
Jing Wang, Zhengqiang Wang, Mingkai Chen, Yong Xiao, Shi Chen, Lianlian Wu, Liwen Yao, Xiaoda Jiang, Jiao Li, Ming Xu, Mengjuan Lin, Yijie Zhu, Renquan Luo, Chenxia Zhang, Xun Li, Honggang Yu |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
npj Digital Medicine, Vol 5, Iss 1, Pp 1-9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2398-6352 |
| DOI: |
10.1038/s41746-022-00729-z |
| Popis: |
Abstract Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1–3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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