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

Endoscopy International Open, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Endosc Int Open 2024; 12(11): E1277-E1284
DOI: 10.1055/a-2422-9214

Original article

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

Autoren

Shan-shan Hu

¹Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
Bowen Duan

²Endoscopy, Sichuan Cancer Hospital and Institute, Chengdu, China (Ringgold ID: RIN92293)
Li Xu

¹Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
Danping Huang

³Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China (Ringgold ID: RIN602121)
Xiaogang Liu

¹Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
Shihao Gou

³Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China (Ringgold ID: RIN602121)
Xiaochen Zhao

⁴Hepatobiliary Pancreatic Surgery, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
Jie Hou

⁵Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
Shirong Tan

⁵Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China (Ringgold ID: RIN89669)
lan ying He

⁶Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China (Ringgold ID: RIN605425)
Ying Ye

⁷Department of Gastroenterology, Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu, China (Ringgold ID: RIN592090)
Xiaoli Xie

⁸Gastroenterology, The First People's Hospital of Longquanyi District Chengdu, Chengdu, China (Ringgold ID: RIN577890)
Hong Shen

¹Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)
Wei-hui Liu

¹Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (Ringgold ID: RIN89669)

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

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

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