Original Article
Deep learning-based diagnostic system for gastrointestinal submucosal tumor under endoscopic ultrasonography
Zhang Chenxia, Li Xun, Yao Liwen, Zhang Jun, Lu Zihua, Wu Huiling, Yu Honggang
Published 2022-07-15
Cite as Chin J Dig, 2022, 42(7): 464-469. DOI: 10.3760/cma.j.cn311367-20220121-00042
Abstract
ObjectiveTo construct a deep learning-based diagnostic system for gastrointestinal submucosal tumor (SMT) under endoscopic ultrasonography (EUS), so as to help endoscopists diagnose SMT.
MethodsFrom January 1, 2019 to December 15, 2021, at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University, 245 patients with SMT confirmed by pathological diagnosis who underwent EUS and endoscopic submucosal dissection were enrolled. A total of 3 400 EUS images were collected. Among the images, 2 722 EUS images were used for training of lesion segmentation model, while 2 209 EUS images were used for training of stromal tumor and leiomyoma classification model; 283 and 191 images were selected as independent test sets to evaluate lesion segmentation model and classification model, respectively. Thirty images were selected as an independent data set for human-machine competition to compare the lesion classification accuracy between lesion classification models and 6 endoscopists. The performance of the segmentation model was evaluated by indexes such as Intersection-over-Union and Dice coefficient. The performance of the classification model was evaluated by accuracy. Chi-square test was used for statistical analysis.
ResultsThe average Intersection-over-Union and Dice coefficient of lesion segmentation model were 0.754 and 0.835, respectively, and the accuracy, recall and F1 score were 95.2%, 98.9% and 97.0%, respectively. Based on the lesion segmentation, the accuracy of classification model increased from 70.2% to 92.1%. The results of human-machine competition showed that the accuracy of classification model in differential diagnosis of stromal tumor and leiomyoma was 86.7% (26/30), which was superior to that of 4 out of the 6 endoscopists(56.7%, 17/30; 56.7%, 17/30; 53.3%, 16/30; 60.0%, 18/30; respectively), and the differences were statistically significant(χ2=7.11, 7.36, 8.10, 6.13; all P<0.05). There was no significant difference between the accuracy of the other 2 endoscopists(76.7%, 23/30; 73.3%, 22/30; respectively) and model(both P<0.05).
ConclusionThis system could be used for the auxiliary diagnosis of SMT under ultrasonic endoscope in the future, and to provide a powerful evidence for the selection of subsequent treatment decisions.
Key words:
Deep learning; Endoscopic ultrasound; Submucosal tumor; Diagnosis
Contributor Information
Zhang Chenxia
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Li Xun
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Yao Liwen
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Zhang Jun
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Lu Zihua
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Wu Huiling
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
