Performance Comparison of the Deep Learning and the Human Endoscopist for Bleeding Peptic Ulcer Disease

Abstract

Purpose Management of peptic ulcer bleeding is clinically challenging. Accurate characterization of the bleeding during endoscopy is key for endoscopic therapy. This study aimed to assess whether a deep learning model can aid in the classification of bleeding peptic ulcer disease. Methods Endoscopic still images of patients (n = 1694) with peptic ulcer bleeding for the last 5 years were retrieved and reviewed. Overall, 2289 images were collected for deep learning model training, and 449 images were validated for the performance test. Two expert endoscopists classified the images into different classes based on their appearance. Four deep learning models, including Mobile Net V2, VGG16, Inception V4, and ResNet50, were proposed and pre-trained by ImageNet with the established convolutional neural network algorithm. A comparison of the endoscopists and trained deep learning model was performed to evaluate the model’s performance on a dataset of 449 testing images. Results The results first presented the performance comparisons of four deep learning models. The Mobile Net V2 presented the optimal performance of the proposal models. The Mobile Net V2 was chosen for further comparing the performance with the diagnostic results obtained by one senior and one novice endoscopists. The sensitivity and specificity were acceptable for the prediction of “normal” lesions in both 3-class and 4-class classifications. For the 3-class category, the sensitivity and specificity were 94.83% and 92.36%, respectively. For the 4-class category, the sensitivity and specificity were 95.40% and 92.70%, respectively. The interobserver agreement of the testing dataset of the model was moderate to substantial with the senior endoscopist. The accuracy of the determination of endoscopic therapy required and high-risk endoscopic therapy of the deep learning model was higher than that of the novice endoscopist. Conclusions In this study, the deep learning model performed better than inexperienced endoscopists. Further improvement of the model may aid in clinical decision-making during clinical practice, especially for trainee endoscopist.

Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature

“…In an Italian study, Forrest Ia/b lesions showed a high interobserver agreement, whereas Forrest II/III lesions exhibited a low agreement [25]. To explore whether AI is useful for identifying the endoscopic characteristics of hemorrhage during endoscopy, our study [26] initiated the proposal of a DL model that can classify endoscopic images with different bleeding risks according to the Forrest classification and using 2378 still endoscopic images from 1694 patients with PUB (Figure 3). The agreement of the model was moderate to substantial with the senior endoscopist on the To explore whether AI is useful for identifying the endoscopic characteristics of hemorrhage during endoscopy, our study [26] initiated the proposal of a DL model that can classify endoscopic images with different bleeding risks according to the Forrest classification and using 2378 still endoscopic images from 1694 patients with PUB (Figure 3).…”

“…Therefore, the DL model has potential use, particularly in aiding young endoscopists in decision making during emergent endoscopy. To explore whether AI is useful for identifying the endoscopic characteristics of hemorrhage during endoscopy, our study [26] initiated the proposal of a DL model that can classify endoscopic images with different bleeding risks according to the Forrest classification and using 2378 still endoscopic images from 1694 patients with PUB (Figure 3). The agreement of the model was moderate to substantial with the senior endoscopist on the testing dataset.…”

Real-World Experience of Chronic Hepatitis C-Related Compensated Liver Cirrhosis Treated with Glecaprevir/Pibrentasvir: A Multicenter Retrospective Study

“…Until now, data on the efficacy and safety of GLE/PIB in patients with compensated cirrhosis are still limited in the real-world setting [15,16], especially for the Asian population. Thus, this study aimed to evaluate the real-world safety and efficacy of GLE/PIB in patients with compensated cirrhosis from five hospitals in the Changhua Christian Care System [9,17].…”

A Nomogram for Predicting Laparoscopic and Endoscopic Cooperative Surgery during the Endoscopic Resection of Subepithelial Tumors of the Upper Gastrointestinal Tract

“…With the improvement of endoscopic techniques, small perforations may be successfully sealed with clips intraoperatively. Large perforations may need laparoscopic surgery, with increased complication rates and prolonged hospitalization days [5,13,[19][20][21]. The perforation rate during ESD for SET is reportedly 4-27.2% [9,[22][23][24][25][26][27].…”