Details

Autor(en) / Beteiligte

• Wang, S X
• Ke, Y
• Liu, Y M
• Liu, S Y
• Song, S B
• He, S
• Zhang, Y M
• Dou, L Z
• Liu, Y
• Liu, X D
• Wu, H R
• Su, F X
• Zhang, F Y
• Zhang, W
• Wang, G Q

Titel

Establishment and clinical validation of an artificial intelligence YOLOv51 model for the detection of precancerous lesions and superficial esophageal cancer in endoscopic procedure

Ist Teil von

• Zhōnghuá zhŏngliú zázhì, 2022-05, Vol.44 (5), p.395

Ort / Verlag

China

Erscheinungsjahr

2022

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• To construct the diagnostic model of superficial esophageal squamous cell carcinoma (ESCC) and precancerous lesions in endoscopic images based on the YOLOv5l model by using deep learning method of artificial intelligence to improve the diagnosis of early ESCC and precancerous lesions under endoscopy. 13, 009 endoscopic esophageal images of white light imaging (WLI), narrow band imaging (NBI) and lugol chromoendoscopy (LCE) were collected from June 2019 to July 2021 from 1, 126 patients at the Cancer Hospital, Chinese Academy of Medical Sciences, including low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, ESCC limited to the mucosal layer, benign esophageal lesions and normal esophagus. By computerized random function method, the images were divided into a training set (11, 547 images from 1, 025 patients) and a validation set (1, 462 images from 101 patients). The YOLOv5l model was trained and constructed with the training set, and the model was validated with the validation set, whil