Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound

Shan-Shan Hu; Bowen Duan; Li Xu; Danping Huang; Xiaogang Liu; Shihao Gou; Xiaochen Zhao; Jie Hou; Shirong Tan; Lan Ying He; Ying Ye; Xiaoli Xie; Hong Shen; Wei-Hui Liu

doi:10.1055/a-2422-9214

Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound

Endosc Int Open. 2024 Nov 7;12(11):E1277-E1284. doi: 10.1055/a-2422-9214. eCollection 2024 Nov.

Authors

Shan-Shan Hu¹, Bowen Duan², Li Xu¹, Danping Huang³, Xiaogang Liu¹, Shihao Gou³, Xiaochen Zhao⁴, Jie Hou⁵, Shirong Tan⁵, Lan Ying He⁶, Ying Ye⁷, Xiaoli Xie⁸, Hong Shen¹, Wei-Hui Liu¹

Affiliations

¹ Department of Gastroenterology and Hepatology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
² Endoscopy, Sichuan Cancer Hospital and Institute, Chengdu, China.
³ Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China.
⁴ Hepatobiliary Pancreatic Surgery, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China.
⁵ Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China.
⁶ Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China.
⁷ Department of Gastroenterology, Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu, China.
⁸ Gastroenterology, The First People's Hospital of Longquanyi District Chengdu, Chengdu, China.

Abstract

Background and study aims Endoscopic ultrasound (EUS) is vital for early pancreatic cancer diagnosis. Advances in artificial intelligence (AI), especially deep learning, have improved medical image analysis. We developed and validated the Modified Faster R-CNN (M-F-RCNN), an AI algorithm using EUS images to assist in diagnosing pancreatic cancer. Methods We collected EUS images from 155 patients across three endoscopy centers from July 2022 to July 2023. M-F-RCNN development involved enhancing feature information through data preprocessing and utilizing an improved Faster R-CNN model to identify cancerous regions. Its diagnostic capabilities were validated against an external set of 1,000 EUS images. In addition, five EUS doctors participated in a study comparing the M-F-RCNN model's performance with that of human experts, assessing diagnostic skill improvements with AI assistance. Results Internally, the M-F-RCNN model surpassed traditional algorithms with an average precision of 97.35%, accuracy of 96.49%, and recall rate of 5.44%. In external validation, its sensitivity, specificity, and accuracy were 91.7%, 91.5%, and 91.6%, respectively, outperforming non-expert physicians. The model also significantly enhanced the diagnostic skills of doctors. Conclusions: The M-F-RCNN model shows exceptional performance in diagnosing pancreatic cancer via EUS images, greatly improving diagnostic accuracy and efficiency, thus enhancing physician proficiency and reducing diagnostic errors.

Keywords: Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE); Endoscopic ultrasonography; Endoscopy Upper GI Tract; Pancreas; Precancerous conditions & cancerous lesions (displasia and cancer) stomach.

The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).