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Dong Z, Zhao X, Zheng H, Zheng H, Chen D, Cao J, Xiao Z, Sun Y, Zhuang Q, Wu S, Xia J, Ning M, Qin B, Zhou H, Bao J, Wan X. Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study. EClinicalMedicine 2024; 73:102656. [PMID: 38828130 PMCID: PMC11137341 DOI: 10.1016/j.eclinm.2024.102656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Background Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas. Methods The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787. Findings The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas]. Interpretation We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making. Funding Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.
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Affiliation(s)
- Zhixia Dong
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangyun Zhao
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hangbin Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - HanYao Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Dafan Chen
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Cao
- Endoscopy Center, Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zili Xiao
- Digestive Endoscopic Department, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yunwei Sun
- Department of Gastroenterology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Zhuang
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wu
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Xia
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Ning
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Hui Zhou
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinsong Bao
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Xinjian Wan
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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