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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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Saadat A, Siddiqui T, Taseen S, Mughal S. Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies. Ann Biomed Eng 2024; 52:1546-1548. [PMID: 37548817 DOI: 10.1007/s10439-023-03343-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
The application of artificial intelligence (AI) in the field of medicine has revolutionised various sectors of the health care system, including robotics surgery, biotechnology, pharmaceutical, evidence-based medicine and advanced research and transplantation techniques. By offering improved 3D imagery of the various organs involved in surgery and perfectly minimising the chances of error, AI aid made complicated surgical procedures more efficient and highly effective, requiring less hands-on. Further, the AI tool helps plastic surgery and aesthetic surgeons in anticipating prognostic surgical markers and post-operative consequences. In addition to enhancing accurate and rapid diagnosis, AI has played a pivotal role in the development and discovery of new drugs. Nevertheless, the application of AI in health care also raises significant challenges and concerns. Incorrect drug recommendations, failure to identify tumours and lesions on imaging modalities and potential bias in data entry and its automatic can risk the life of patients on a large scale. Additionally, breaching patient data privacy may raise concerns about cybersecurity issues, further compromised by growing dependency on AI which can result in massive unemployment. In short, AI has played a pivotal role in health care; however, addressing the in-transparencies is critical to ensure safe, ethical and more effective implementation in the dynamic field of medicine.
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Affiliation(s)
- Ayesha Saadat
- Department of Medicine, Karachi Medical and Dental College, North Nazimabad Town, Karachi, 74700, Pakistan
| | - Tasmiyah Siddiqui
- Department of Internal Medicine, Dow University of Health Sciences, Baba-e-Urdu Road, Saddar, Karachi, 74200, Pakistan
| | - Shafaq Taseen
- Department of Medicine, Karachi Medical and Dental College, North Nazimabad Town, Karachi, 74700, Pakistan
| | - Sanila Mughal
- Department of Medicine, Dow University of Health Sciences, Baba-e-Urdu Road, Saddar, Karachi, 74200, Pakistan.
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Cai M, Song B, Deng Y, Gao P, Cai S, Yalikong A, Xu E, Zhong Y, Yu J, Zhou P. Automatically optimized radiomics modeling system for small gastric submucosal tumor (<2 cm) discrimination based on EUS images. Gastrointest Endosc 2024; 99:537-547.e4. [PMID: 37956896 DOI: 10.1016/j.gie.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 10/14/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND AIMS The clinical management of small gastric submucosal tumors (SMTs) (<2 cm) faces a non-negligible challenge because of the lack of guideline consensus and effective diagnostic tools. This article develops an automatically optimized radiomics modeling system (AORMS) based on EUS images to diagnose and evaluate SMTs. METHODS A total of 205 patients with EUS images of small gastric SMTs (<2 cm) were retrospectively enrolled in the development phase of AORMS for the diagnosis and the risk stratification of GI stromal tumor (GIST). A total of 178 patients with images from different centers were prospectively enrolled in the independent testing phase. The performance of AORMS was compared to that of endoscopists in the development set and evaluated in the independent testing set. RESULTS AORMS demonstrated an area under the curve (AUC) of 0.762 for the diagnosis of GIST and 0.734 for the risk stratification of GIST, respectively. In the independent testing set, AORMS achieved an AUC of 0.770 and 0.750 for the diagnosis and risk stratification of small GISTs, respectively. In comparison, the AUCs of 5 experienced endoscopists ranged from 0.501 to 0.608 for diagnosing GIST and from 0.562 to 0.748 for risk stratification. AORMS outperformed experienced endoscopists by more than 20% in diagnosing GIST. CONCLUSIONS AORMS implements automatic parameter selection, which enhances its robustness and clinical applicability. It has demonstrated good performance in the diagnosis and risk stratification of GISTs, which could aid endoscopists in the diagnosis of small gastric SMTs (<2 cm).
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Affiliation(s)
- Mingyan Cai
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Baohui Song
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Yinhui Deng
- MingGe Research, Fudan University Science Park, Shanghai, China; Biomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Pingting Gao
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Shilun Cai
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Ayimukedisi Yalikong
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Enpan Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China
| | - Yunshi Zhong
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China; Department of Endoscopy, Zhongshan Hospital Xuhui Branch, Fudan University, Shanghai, China.
| | - Jinhua Yu
- Biomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, China.
| | - Pinghong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Endoscopic Minimally Invasive Collaborative Innovation Center, Shanghai, China.
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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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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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Chen H, Liu SY, Huang SH, Liu M, Chen GX. Applications of artificial intelligence in gastroscopy: a narrative review. J Int Med Res 2024; 52:3000605231223454. [PMID: 38235690 PMCID: PMC10798083 DOI: 10.1177/03000605231223454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Gastroscopy, a critical tool for the diagnosis of upper gastrointestinal diseases, has recently incorporated artificial intelligence (AI) technology to alleviate the challenges involved in endoscopic diagnosis of some lesions, thereby enhancing diagnostic accuracy. This narrative review covers the current status of research concerning various applications of AI technology to gastroscopy, then discusses future research directions. By providing this review, we hope to promote the integration of gastroscopy and AI technology, with long-term clinical applications that can assist patients.
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Affiliation(s)
- Hu Chen
- The First Clinical Medical School, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Shi-yu Liu
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Si-hui Huang
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Min Liu
- School of Chemical Engineering & Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Guang-xia Chen
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China
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Lu Y, Wu J, Hu M, Zhong Q, Er L, Shi H, Cheng W, Chen K, Liu Y, Qiu B, Xu Q, Lai G, Wang Y, Luo Y, Mu J, Zhang W, Zhi M, Sun J. Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers. Gut Liver 2023; 17:874-883. [PMID: 36700302 PMCID: PMC10651383 DOI: 10.5009/gnl220347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/19/2022] [Accepted: 10/07/2022] [Indexed: 01/27/2023] Open
Abstract
Background/Aims The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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Affiliation(s)
- Yi Lu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiachuan Wu
- Digestive Endoscopy Center, Guangdong Second Provincial General Hospital, Sun Yat-sen University, Guangzhou, China
| | - Minhui Hu
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qinghua Zhong
- Department of Endoscopic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Limian Er
- Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Huihui Shi
- Department of Endoscopy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Weihui Cheng
- Department of Gastroenterology, Yangjiang Hospital of Traditional Chinese Medicine, Yangjiang, China
| | - Ke Chen
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuan Liu
- Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bingfeng Qiu
- Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
| | - Qiancheng Xu
- Department of Gastroenterology, Zhoushan Hospital of Zhejiang Province, Zhoushan, China
| | - Guangshun Lai
- Department of Gastroenterology, Lianjiang People’s Hospital, Lianjiang, China
| | - Yufeng Wang
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Yuxuan Luo
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Jinbao Mu
- Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China
| | - Wenjie Zhang
- Tianjin Center for Medical Devices Evaluation and Inspection, Tianjin, China
| | - Min Zhi
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiachen Sun
- Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Gomes RSA, de Oliveira GHP, de Moura DTH, Kotinda APST, Matsubayashi CO, Hirsch BS, Veras MDO, Ribeiro Jordão Sasso JG, Trasolini RP, Bernardo WM, de Moura EGH. Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis. World J Gastrointest Endosc 2023; 15:528-539. [PMID: 37663113 PMCID: PMC10473903 DOI: 10.4253/wjge.v15.i8.528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/15/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology. AIM To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer. METHODS Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed. RESULTS Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819. CONCLUSION AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.
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Affiliation(s)
- Rômulo Sérgio Araújo Gomes
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Diogo Turiani Hourneaux de Moura
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Ana Paula Samy Tanaka Kotinda
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Carolina Ogawa Matsubayashi
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Bruno Salomão Hirsch
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Matheus de Oliveira Veras
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Roberto Paolo Trasolini
- Division of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Wanderley Marques Bernardo
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
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Lu Y, Chen L, Wu J, Er L, Shi H, Cheng W, Chen K, Liu Y, Qiu B, Xu Q, Feng Y, Tang N, Wan F, Sun J, Zhi M. Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors. Therap Adv Gastroenterol 2023; 16:17562848231177156. [PMID: 37274299 PMCID: PMC10233610 DOI: 10.1177/17562848231177156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Background Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine. Objectives We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs. Design This was a retrospective study with external validation. Methods We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts. Results A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively. Conclusion We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs. Registration The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).
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Affiliation(s)
- Yi Lu
- Department of Gastrointestinal Endoscopy,
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases,
The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s
Republic of China
| | - Lu Chen
- Department of Internal Medicine, Advent Health
Palm Coast, Palm Coast, FL, USA
| | - Jiachuan Wu
- Digestive Endoscopy Center, Guangdong Second
Provincial General Hospital, Guangzhou, People’s Republic of China
| | - Limian Er
- Department of Endoscopy, The Fourth Hospital of
Hebei Medical University, Shijiazhuang, People’s Republic of China
| | - Huihui Shi
- Department of Endoscopy, The Fourth Hospital of
Hebei Medical University, Shijiazhuang, People’s Republic of China
| | - Weihui Cheng
- Department of Gastroenterology, Yangjiang
Hospital of Traditional Chinese Medicine, Yangjiang, People’s Republic of
China
| | - Ke Chen
- Department of Endoscopy, Fudan University
Shanghai Cancer Center, Shanghai, People’s Republic of China
| | - Yuan Liu
- Department of Endoscopy, Fudan University
Shanghai Cancer Center, Shanghai, People’s Republic of China
| | - Bingfeng Qiu
- Department of Gastroenterology, Zhoushan
Hospital of Zhejiang Province, Zhoushan, People’s Republic of China
| | - Qiancheng Xu
- Department of Gastroenterology, Zhoushan
Hospital of Zhejiang Province, Zhoushan, People’s Republic of China
| | - Yue Feng
- Tianjin Economic-Technological Development
Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin,
People’s Republic of China
| | - Nan Tang
- Tianjin Center for Medical Devices Evaluation
and Inspection, Tianjin, People’s Republic of China
| | - Fuchuan Wan
- Tianjin Economic-Technological Development
Area (TEDA) Yujin Artificial Intelligence Medical Technology Co, Ltd,
Tianjin, People’s Republic of China
| | - Jiachen Sun
- Department of Gastrointestinal Endoscopy,
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases,
The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng
Road, Guangzhou 510655, People’s Republic of China
| | - Min Zhi
- Department of Gastroenterology, Guangdong
Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth
Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road,
Guangzhou 510655, People’s Republic of China
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11
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Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis. Surg Endosc 2023; 37:1649-1657. [PMID: 36100781 DOI: 10.1007/s00464-022-09597-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/25/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images. METHODS PubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy. RESULTS Overall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89-0.95), 0.82 (95% CI 0.75-0.87), 4.55 (95% CI 2.64-7.84), and 0.12 (95% CI 0.07-0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83-175.69) and 0.950 (Q* = 0.891). CONCLUSIONS AI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future.
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Pallio S, Crinò SF, Maida M, Sinagra E, Tripodi VF, Facciorusso A, Ofosu A, Conti Bellocchi MC, Shahini E, Melita G. Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours. Cancers (Basel) 2023; 15:cancers15041285. [PMID: 36831627 PMCID: PMC9954263 DOI: 10.3390/cancers15041285] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Gastrointestinal Stromal Tumors (GISTs) are subepithelial lesions (SELs) that commonly develop in the gastrointestinal tract. GISTs, unlike other SELs, can exhibit malignant behavior, so differential diagnosis is critical to the decision-making process. Endoscopic ultrasound (EUS) is considered the most accurate imaging method for diagnosing and differentiating SELs in the gastrointestinal tract by assessing the lesions precisely and evaluating their malignant risk. Due to their overlapping imaging characteristics, endosonographers may have difficulty distinguishing GISTs from other SELs using conventional EUS alone, and the collection of tissue samples from these lesions may be technically challenging. Even though it appears to be less effective in the case of smaller lesions, histology is now the gold standard for achieving a final diagnosis and avoiding unnecessary and invasive treatment for benign SELs. The use of enhanced EUS modalities and elastography has improved the diagnostic ability of EUS. Furthermore, recent advancements in artificial intelligence systems that use EUS images have allowed them to distinguish GISTs from other SELs, thereby improving their diagnostic accuracy.
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Affiliation(s)
- Socrate Pallio
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy
| | | | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, 93100 Caltanissetta, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, 90015 Cefalù, Italy
| | | | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, 71100 Foggia, Italy
| | - Andrew Ofosu
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH 45201, USA
| | | | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology—IRCCS “Saverio de Bellis” Castellana Grotte, 70013 Castellana Grotte, Italy
| | - Giuseppinella Melita
- Human Pathology of Adult and Child Department, University of Messina, 98100 Messina, Italy
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13
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Kim D, Cho S, Park SY, You HS, Jung YW, Cho SH, Park C, Kim HS, Choi S, Rew J. Natural Course of Asymptomatic Upper Gastrointestinal Subepithelial Lesion of 2 cm or Less in Size. J Clin Med 2022; 11:jcm11247506. [PMID: 36556122 PMCID: PMC9787346 DOI: 10.3390/jcm11247506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
There is limited evidence of a natural course of an upper gastrointestinal (UGI)-subepithelial lesion (SEL) of 2 cm or less in size. This study aims to determine the natural course of UGI-SELs and find the risk factors of the endoscopic and endoscopic ultrasonography (EUS) findings associated with an increase in size. The medical records of 2539 patients with UGI-SELs between 2004 and 2016 were reviewed retrospectively. A total of 672 SELs of 2 cm or less in size were analyzed through EUS and followed up for at least 36 months. The mean follow-up duration was 68 months (range: 36-190 months), and 97 SELs (14.4%) showed an increase in size with a mean increase rate of 1.2 mm/year. Initial size (aOR 1.03, 95% confidence interval (CI) 1.01-1.06), an endoscopic finding of a hemorrhagic spot (aOR 3.13, 95% CI 1.14-8.60), and an EUS finding of a lesion in the fourth layer (aOR 1.87, 95% CI (1.21-2.88) were related to an increase in size. An endoscopic finding of translucidity (aOR 0.28, 95% CI (0.10-0.76) and an EUS finding of calcification (aOR 0.30, 95% CI 0.09-0.95) were inversely related to an increase in size. There was no death related to UGI-SELs during the follow-up. While most UGI-SELs of 2 cm or less in size showed no significant size change and favorable prognosis, an individualized follow-up strategy needs to be considered in case of the presence of hemorrhagic spots and lesions in the fourth layer.
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14
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Ortiz Zúñiga O, Fernández Esparrach MG, Daca M, Pellisé M. Artificial intelligence in gastrointestinal endoscopy - Evolution to a new era. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2022; 114:605-615. [PMID: 35770604 DOI: 10.17235/reed.2022.8961/2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) systems based on machine learning have evolved in the last few years with an increasing applicability in gastrointestinal endoscopy. Thanks to AI, an image (input) can be transformed into a clinical decision (output). Although AI systems have been initially studied to improve detection (CADe) and characterization of colorectal lesions (CADx), other indications are being currently investigated as detection of blind spots, scope guidance, or delineation/measurement of lesions. The objective of these review is to summarize the current evidence on applicability of AI systems in gastrointestinal endoscopy, highlight strengths and limitations of the technology and review regulatory and ethical aspects for its general implementation in gastrointestinal endoscopy.
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Affiliation(s)
| | | | - María Daca
- Gastroenterología, Hospital Clínic Barcelona, España
| | - María Pellisé
- Gastroenterología, Hospital Clínic Barcelona, España
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15
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Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists. Eur Radiol 2022; 32:8692-8705. [PMID: 35616733 DOI: 10.1007/s00330-022-08842-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/13/2022] [Accepted: 04/25/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVES Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. METHODS We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. RESULTS DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). CONCLUSION DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM. KEY POINTS • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.
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16
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Ye XH, Zhao LL, Wang L. Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta-analysis. J Dig Dis 2022; 23:253-261. [PMID: 35793389 DOI: 10.1111/1751-2980.13110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/21/2022] [Accepted: 07/01/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Gastrointestinal stromal tumors (GISTs) are thought to have a malignant potential. However, utilizing endoscopic ultrasound (EUS) to differentiate GISTs from other types of subepithelial lesions (SELs) remains challenging. Artificial intelligence (AI)-based diagnostic systems for EUS have been reported to have a promising performance, although the results of the previous studies remain controversial. In this meta-analysis, we aimed to assess the diagnostic accuracy of AI-based EUS in distinguishing GISTs from other SELs. METHODS A literature search was conducted on MEDLINE and EMBASE databases to identify relevant articles. The sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUROC) of eligible studies were analyzed. RESULTS Seven studies were eligible for the final analysis. The combined sensitivity and specificity of AI-based EUS were 0.93 (95% confidence interval [CI] 0.88-0.96) and 0.78 (95% CI 0.67-0.87), respectively. The overall diagnostic odds ratio of AI-based EUS for GISTs was 36.74 (95% CI 17.69-76.30) with an AUROC of 0.94. CONCLUSIONS AI-based EUS showed high diagnostic ability and might help better differentiate GISTs from other SELs. More prospective studies on the diagnosis of GISTs using AI-based EUS are warranted in clinical setting.
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Affiliation(s)
- Xiao Hua Ye
- Department of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang Province, China
| | - Lin Lin Zhao
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Lei Wang
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
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17
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Mi JW, Wang JQ, Liu J, Zhang LX, Du HW, Zhao DQ. The Value of Endoscopic Ultrasonography in the Endoscopic Resection of Gastrointestinal Stromal Tumors. Int J Gen Med 2021; 14:5149-5157. [PMID: 34511997 PMCID: PMC8421251 DOI: 10.2147/ijgm.s319762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/11/2021] [Indexed: 11/23/2022] Open
Abstract
Objective This study aimed to explore the clinical value of endoscopic ultrasonography (EUS) in the endoscopic resection of gastrointestinal stromal tumors (GISTs). Methods A retrospective study of 92 patients who were confirmed to have GISTs by endoscopic resection after EUS examination was conducted. The preoperative features of the EUS examination, ultrasound diagnosis, endoscopic resection methods, surgical procedures, complications, and complete degree of lesion resection were recorded. And 16 patients who were diagnosed by endoscopy and EUS and confirmed by surgical operation were included and analyzed in the subsequent part of the investigation (gastroscopy and EUS image analysis, EUS image and risk classification). Results The preoperative diagnosis rate of EUS and postoperative pathological diagnosis of GISTs was 78.7% (85/108), and the presence of a non-homogeneous echo and liquid anechoic zone in GISTs often indicated higher risk (P < 0.05). There was a positive correlation between tumor size and risk (P < 0.05). Conclusion The endoscopic resection of GISTs is feasible and safe. EUS is of great significance for the diagnosis and risk assessment of GISTs and can assist in the endoscopic resection of GISTs.
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Affiliation(s)
- Jian-Wei Mi
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Jia-Qi Wang
- Basic Medical College, Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Jie Liu
- Department of Trauma Emergency, Medical Noncommissioned Officer School, Army Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Li-Xian Zhang
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Hong-Wei Du
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
| | - Dong-Qiang Zhao
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, People's Republic of China
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Goto O, Kaise M, Iwakiri K. Advancements in the Diagnosis of Gastric Subepithelial Tumors. Gut Liver 2021; 16:321-330. [PMID: 34456187 PMCID: PMC9099397 DOI: 10.5009/gnl210242] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/29/2021] [Accepted: 07/06/2021] [Indexed: 11/04/2022] Open
Abstract
A diagnosis of subepithelial tumors (SETs) is sometimes difficult due to the existence of overlying mucosa on the lesions, which hampers optical diagnosis by conventional endoscopy and tissue sampling with standard biopsy forceps. Imaging modalities, by using computed tomography and endoscopic ultrasonography (EUS) are mandatory to noninvasively collect the target's information and to opt candidates for further evaluation. Particularly, EUS is an indispensable diagnostic modality for assessing the lesions precisely and evaluating the possibility of malignancy. The diagnostic ability of EUS appears increased by the combined use of contrast-enhancement or elastography. Histology is the gold standard for obtaining the final diagnosis. Tissue sampling requires special techniques to break the mucosal barrier. Although EUS-guided fine-needle aspiration (EUS-FNA) is commonly applied, mucosal cutting biopsy and mucosal incision-assisted biopsy are comparable methods to definitively obtain tissues from the exposed surface of lesions and seem more useful than EUS-FNA for small SETs. Recent advancements in artificial intelligence (AI) have a potential to drastically change the diagnostic strategy for SETs. Development and establishment of noninvasive methods including AI-assisted diagnosis are expected to provide an alternative to invasive, histological diagnosis.
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Affiliation(s)
- Osamu Goto
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Mitsuru Kaise
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Katsuhiko Iwakiri
- Department of Gastroenterology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
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