Use of Hemostatic Forceps as a Preoperative Rescue Therapy for Bleeding Peptic Ulcers

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

With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people's lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists' clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.

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

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.

Use of dual knife for large pedunculated colorectal polyps

BACKGROUND

Endoscopic resection of large pedunculated colorectal polyps is technically difficult, especially when the polyps are so large that it is either not possible or too difficult to resect them using a conventional polypectomy snare.

AIM

To facilitate the removal of pedunculated colorectal polyps, we developed a new technique using a combination of a dual knife and preventive hemostatic procedures.

METHODS

Nine patients (5 men and 4 women; mean age, 59.8 y; range, 49 to 79 y) with pedunculated polyps >2 cm in diameter were treated with this technique. A dual knife and endoclips or an endoloop were used as needed.

RESULTS

All lesions, except for 1 polyp, were resected endoscopically. The mean time for complete resection was 8.5 minutes (range, 1.5 to 16 min). The procedure time decreased significantly after the fifth case (168 vs. 746 s, P=0.0009). No hemorrhage, perforation, or other complications occurred during follow-up among the endoscopically resected cases.

CONCLUSIONS

Combined use of a dual knife and prophylactic hemostatic procedures is a feasible alternative technique for removing large pedunculated colorectal polyps when conventional snare resection is impossible or difficult.