Construction and validation of an artificial intelligence system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions
10.3760/cma.j.cn321463-20240805-00209
- VernacularTitle:基于多特征拟合诊断胃褪色调肿瘤性病变的人工智能系统的构建和验证
- Author:
Xiaoquan ZENG
1
;
Zehua DONG
1
;
Yanxia LI
1
;
Yunchao DENG
1
;
Honggang YU
1
;
Mingkai CHEN
1
Author Information
1. 武汉大学人民医院消化内科 消化系统疾病湖北省重点实验室，武汉 430060
- Publication Type:Journal Article
- Keywords:
Machine learning;
Artificial intelligence;
Endoscopy;
Gastric whitish lesions
- From:
Chinese Journal of Digestive Endoscopy
2025;42(8):596-601
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.
