Development and clinical feasibility of intelligent quality control system in gastroscopy
10.3760/cma.j.cn311367-20200623-00400
- VernacularTitle:胃镜智能质控系统研发和临床可行性研究
- Author:
Jingran SU
1
;
Zhen LI
;
Xiaoyun YANG
;
Xiuli ZUO
;
Yanqing LI
Author Information
1. 山东大学齐鲁医院消化内科 胃肠疾病转化医学实验室 胃肠道肿瘤机器人精准诊疗工程实验室，济南 250012
- From:
Chinese Journal of Digestion
2020;40(11):751-757
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop intelligent quality-control system (IQCS) based on deep convolutional neural networks (DCNN), and to prospectively evaluate the clinical feasibility of this system.Methods:Aimed at quality control objectives during gastroscopy such as the observation integrity of gastric mucosal, gastric mucosa visibility, time spent on gastroendoscopy and suspicious gastric cancer detection, four DCNN models including gastroscopic scanning location recognition model, gastric mucosa visibility recognition model, in vivo and in vitro identification model and gastric cancer detection model were designed. A total of 98 385 white light gastroscopy images were retrospectively collected from multiple centers for training and testing the DCNN models. The accuracy, sensitivity and specificity of each model were calculated and the receiver operating characteristic (ROC) curve was drawn. The models were integrated and formed the multi-function integrated IQCS. At the center of gastroendoscopy, Qilu Hospital of Shandong University, 100 consecutive patients who underwent routine gastroscopy were prospectively enrolled. The feasibility of IQCS in real clinical practice was evaluated. The condition of each quality control function of the system (average error point out or correct rate) and the detection of lesions after the examination were recorded. Results:The accuracy, sensitivity and specificity of the model of gastroscopic scanning location recognition to identify each site were 98.40% to 99.85%, 61.95% to 100.00% and 98.65% to 100.00%, respectively; the area under curve (AUC) of ROC curve ranged from 0.997 6 to 1.000 0. The accuracy, sensitivity and specificity of the model of gastric mucosa visibility recognition to identity the mucosal visibility were 97.02% to 98.27%, 85.14% to 99.28% and 93.72% to 100.00%, respectively. The accuracy, sensitivity and specificity of the model of in vivo and in vitro identification were 97.27%, 99.85% and 94.50%, respectively; the AUC of ROC was 0.961 5. The accuracy, sensitivity and specificity of the model of gastric cancer detection were 95.92%, 95.64% and 96.05%, respectively; the AUC of ROC was 0.975 9. The results of feasibility evaluation of IQCS indicated that in the quality control of gastric mucosa observation integrity, the system average error was 0.32 time/case; in the quality control of mucosal visibility, the system average error was 0.47 time/case; the correct rate of intelligent timing during gastroscopy was 96.00%, in the quality control of suspicious gastric cancer detection, the system average error was 0.36 time/case. A total of 3 cases of gastric cancer and 1 case of high grade gastric intraepithelial neoplasia were detected. The system could accurately identify the location. Conclusions:Gastroscopy IQCS can accurately achieve quality control in the observation integrity of gastric mucosa, gastric mucosa visibility, time spent on gastroendoscopy and suspicious gastric cancer detection in actual examination, which makes accurate and efficient gastroscopy quality control possible.
