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Matsubayashi CO, Cheng S, Hulchafo I, Zhang Y, Tada T, Buxbaum JL, Ochiai K. Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market. Dig Liver Dis 2024; 56:1156-1163. [PMID: 38763796 DOI: 10.1016/j.dld.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024]
Abstract
Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.
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Affiliation(s)
- Carolina Ogawa Matsubayashi
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo, Brasil; AI Medical Service Inc., Tokyo, Japan.
| | - Shuyan Cheng
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Ismael Hulchafo
- Columbia University School of Nursing, New York, NY 10032, USA
| | - Yifan Zhang
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan; Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - James L Buxbaum
- Division of Gastrointestinal and Liver Diseases, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Colon and Rectal Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Chang YH, Shin CM, Lee HD, Park J, Jeon J, Cho SJ, Kang SJ, Chung JY, Jun YK, Choi Y, Yoon H, Park YS, Kim N, Lee DH. Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions. J Gastric Cancer 2024; 24:327-340. [PMID: 38960891 PMCID: PMC11224715 DOI: 10.5230/jgc.2024.24.e28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 07/05/2024] Open
Abstract
PURPOSE Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. MATERIALS AND METHODS We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). RESULTS ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. CONCLUSIONS ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.
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Affiliation(s)
- Young Hoon Chang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Cheol Min Shin
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Hae Dong Lee
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | | | | | - Soo-Jeong Cho
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Seung Joo Kang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Jae-Yong Chung
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yu Kyung Jun
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yonghoon Choi
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hyuk Yoon
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Young Soo Park
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Nayoung Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong Ho Lee
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
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Horiuchi Y, Hirasawa T, Fujisaki J. Endoscopic Features of Undifferentiated-Type Early Gastric Cancer in Patients with Helicobacter pylori-Uninfected or -Eradicated Stomachs: A Comprehensive Review. Gut Liver 2024; 18:209-217. [PMID: 37855088 PMCID: PMC10938157 DOI: 10.5009/gnl230106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 03/16/2024] Open
Abstract
Since the indications for endoscopic submucosal dissection have been expanded to include undifferentiated-type early gastric cancers, improvements in preoperative diagnostic ability have been an area of research. There are also concerns about the impact on the diagnosis of Helicobacter pylori infection. Based on our previous studies, in undifferentiated-type early gastric cancers, magnifying endoscopy with narrow-band imaging is useful for delineating the demarcation regardless of the tumor size. Additionally, inflammatory cell infiltration appears to be a cause of misdiagnosis, suggesting that the resolution of inflammation could contribute to the accurate diagnosis of demarcations. As such, the accuracy of demarcation in eradicated and uninfected cases is higher than that in non-eradicated cases. The common features of the endoscopic findings were discoloration under white-light imaging and a predominance of sites in the lower and middle regions. The uninfected group was characterized by smaller tumor size, flat type, more extended intervening parts in magnifying endoscopy with narrow-band imaging, and pure signet ring cell carcinoma. In contrast, the eradication and non-eradication groups were characterized by larger tumor size, depressed type, and wavy microvessels in magnifying endoscopy with narrow-band imaging. In this comprehensive review, as described above, we discuss the diagnosis of demarcation of undifferentiated-type early gastric cancers, undifferentiated-type early gastric cancers that developed following H. pylori eradication, and H. pylori-uninfected undifferentiated-type early gastric cancers, with a focus on studies with self-examination and endoscopic findings and describe the future direction.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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Shi Y, Fan H, Li L, Hou Y, Qian F, Zhuang M, Miao B, Fei S. The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis. World J Surg Oncol 2024; 22:40. [PMID: 38297303 PMCID: PMC10832162 DOI: 10.1186/s12957-024-03321-9] [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: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. METHODS We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. RESULTS Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). CONCLUSION ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
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Affiliation(s)
- Yiheng Shi
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Haohan Fan
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Li Li
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Yaqi Hou
- College of Nursing, Yangzhou University, Yangzhou, 225009, China
| | - Feifei Qian
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Mengting Zhuang
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Bei Miao
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Institute of Digestive Diseases, Xuzhou Medical University, 84 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
| | - Sujuan Fei
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China.
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5
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Horiuchi Y, Hirasawa T, Fujisaki J. Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. Clin Endosc 2024; 57:11-17. [PMID: 38178327 PMCID: PMC10834286 DOI: 10.5946/ce.2023.173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 01/06/2024] Open
Abstract
Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [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: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Wang SH, Chen G, Zhong X, Lin T, Shen Y, Fan X, Cao L. Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022. Front Oncol 2023; 13:1215729. [PMID: 37519796 PMCID: PMC10382324 DOI: 10.3389/fonc.2023.1215729] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Background Artificial intelligence (AI) is widely applied in cancer field nowadays. The aim of this study is to explore the hotspots and trends of AI in cancer research. Methods The retrieval term includes four topic words ("tumor," "cancer," "carcinoma," and "artificial intelligence"), which were searched in the database of Web of Science from January 1983 to December 2022. Then, we documented and processed all data, including the country, continent, Journal Impact Factor, and so on using the bibliometric software. Results A total of 6,920 papers were collected and analyzed. We presented the annual publications and citations, most productive countries/regions, most influential scholars, the collaborations of journals and institutions, and research focus and hotspots in AI-based cancer research. Conclusion This study systematically summarizes the current research overview of AI in cancer research so as to lay the foundation for future research.
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Affiliation(s)
- Sui-Han Wang
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guoqiao Chen
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xin Zhong
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Tianyu Lin
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yan Shen
- Department of General Surgery, The First People’s Hospital of Yu Hang District, Hangzhou, China
| | - Xiaoxiao Fan
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Liping Cao
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Kamitani Y, Nonaka K, Misumi Y, Isomoto H. Safe and Efficient Procedures and Training System for Endoscopic Submucosal Dissection. J Clin Med 2023; 12:jcm12113692. [PMID: 37297887 DOI: 10.3390/jcm12113692] [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: 03/05/2023] [Revised: 05/13/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Recent improvements in endoscopists' skills and technological advances have allowed endoscopic submucosal dissection (ESD) to become a standard treatment in general hospitals. As this treatment entails a high risk of accidental perforation or hemorrhage, therapeutic procedures and training methods that enable ESD to be conducted more safely and efficiently are constantly being developed. This article reviews the therapeutic procedures and training methods used to improve the safety and efficiency of ESD and describes the ESD training system used in a Japanese university hospital at which the number of ESD procedures has gradually increased in a newly established Department of Digestive Endoscopy. During the establishment of this department, the ESD perforation rate was zero among all procedures, including those conducted by trainees.
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Affiliation(s)
- Yu Kamitani
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan
- Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago 683-8504, Japan
| | - Kouichi Nonaka
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan
| | - Yoshitsugu Misumi
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan
| | - Hajime Isomoto
- Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago 683-8504, Japan
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Ma L, Su X, Ma L, Gao X, Sun M. Deep learning for classification and localization of early gastric cancer in endoscopic images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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10
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Ochiai K, Ozawa T, Shibata J, Ishihara S, Tada T. Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy. Diagnostics (Basel) 2022; 12:diagnostics12123153. [PMID: 36553160 PMCID: PMC9777622 DOI: 10.3390/diagnostics12123153] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/07/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.
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Affiliation(s)
- Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tsuyoshi Ozawa
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
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12
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Luo Q, Yang H, Hu B. Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection. J Dig Dis 2022; 23:666-674. [PMID: 36661411 DOI: 10.1111/1751-2980.13154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.
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Affiliation(s)
- Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Yang H, Wu Y, Yang B, Wu M, Zhou J, Liu Q, Lin Y, Li S, Li X, Zhang J, Wang R, Xie Q, Li J, Luo Y, Tu M, Wang X, Lan H, Bai X, Wu H, Zeng F, Zhao H, Yi Z, Zeng F. Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm. Gastrointest Endosc 2022; 96:787-795.e6. [PMID: 35718070 DOI: 10.1016/j.gie.2022.06.011] [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: 01/30/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility. METHODS In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (3:1:1) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise: expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience. RESULTS The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001). CONCLUSIONS The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.
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Affiliation(s)
- Hang Yang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yu Wu
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Bo Yang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Zhou
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qin Liu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yifei Lin
- Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shilin Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jie Zhang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Rui Wang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qianrong Xie
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jingqi Li
- College of Aulin, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Yue Luo
- College of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Mengjie Tu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Department of Surgery, Shantou University Medical College, Shantou, Guangdong, China
| | - Xiao Wang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Haitao Lan
- Department of Sichuan, Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Xuesong Bai
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Huaping Wu
- Department of Cardiac &Vascular Surgery, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Fanwei Zeng
- Department of Spinal Surgery, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China
| | - Hong Zhao
- Department of Hepatobiliary Surgery, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Zhang Yi
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
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The Diagnosis of Early Gastric Cancer Based on Medical Imaging Technology and Mathematical Modeling. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8721654. [PMID: 36226247 PMCID: PMC9550491 DOI: 10.1155/2022/8721654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/05/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022]
Abstract
The key to reducing the mortality of gastric cancer is early detection, early diagnosis, and early treatment of gastric cancer. Early diagnosis of gastric cancer is the key to early detection and diagnosis of gastric cancer. Early diagnosis and treatment of gastric cancer is of great significance for improving the curative effect and reducing mortality of gastric cancer. The purpose of this paper is to study the diagnosis of early gastric cancer based on medical imaging techniques and mathematical modeling. The effect of W-DeepLab network-assisted diagnosis of images under white light was analyzed, and the value of Narrow Band Imaging and Blue Laser Imaging in the diagnosis of early gastric cancer was compared. Because Blue Laser Imaging endoscopy can clearly observe the demarcation line and microvascular morphology; but when using Narrow Band Imaging observation, part of the demarcation line and microvascular morphology is not observed. The results show that Blue Laser Imaging is brighter than Narrow Band Imaging's endoscopic images, and it is easier to observe the microstructure of lesions under endoscopy, so as to accurately determine the nature of lesions.
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15
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Zeng Q, Li H, Zhu Y, Feng Z, Shu X, Wu A, Luo L, Cao Y, Tu Y, Xiong J, Zhou F, Li Z. Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer. Front Med (Lausanne) 2022; 9:986437. [PMID: 36262277 PMCID: PMC9573999 DOI: 10.3389/fmed.2022.986437] [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: 07/05/2022] [Accepted: 09/09/2022] [Indexed: 01/19/2023] Open
Abstract
Background This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847–0.956) and 0.915 (95% CI: 0.850–0.981) in the internal validation and external validation cohorts, respectively. Conclusion We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.
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Affiliation(s)
- Qingwen Zeng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China,Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zongfeng Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China,Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xufeng Shu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Ahao Wu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Lianghua Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Cao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jianbo Xiong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China,Fuqing Zhou,
| | - Zhengrong Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China,Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China,*Correspondence: Zhengrong Li,
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16
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Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol 2022; 12:945904. [PMID: 35992850 PMCID: PMC9389533 DOI: 10.3389/fonc.2022.945904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background and aim Magnifying image-enhanced endoscopy was demonstrated to have higher diagnostic accuracy than white-light endoscopy. However, differentiating early gastric cancers (EGCs) from benign lesions is difficult for beginners. We aimed to determine whether the computer-aided model for the diagnosis of gastric lesions can be applied to videos rather than still images. Methods A total of 719 magnifying optical enhancement images of EGCs, 1,490 optical enhancement images of the benign gastric lesions, and 1,514 images of background mucosa were retrospectively collected to train and develop a computer-aided diagnostic model. Subsequently, 101 video segments and 671 independent images were used for validation, and error frames were labeled to retrain the model. Finally, a total of 117 unaltered full-length videos were utilized to test the model and compared with those diagnostic results made by independent endoscopists. Results Except for atrophy combined with intestinal metaplasia (IM) and low-grade neoplasia, the diagnostic accuracy was 0.90 (85/94). The sensitivity, specificity, PLR, NLR, and overall accuracy of the model to distinguish EGC from non-cancerous lesions were 0.91 (48/53), 0.78 (50/64), 4.14, 0.12, and 0.84 (98/117), respectively. No significant difference was observed in the overall diagnostic accuracy between the computer-aided model and experts. A good level of kappa values was found between the model and experts, which meant that the kappa value was 0.63. Conclusions The performance of the computer-aided model for the diagnosis of EGC is comparable to that of experts. Magnifying the optical enhancement model alone may not be able to deal with all lesions in the stomach, especially when near the focus on severe atrophy with IM. These results warrant further validation in prospective studies with more patients. A ClinicalTrials.gov registration was obtained (identifier number: NCT04563416). Clinical Trial Registration ClinicalTrials.gov, identifier NCT04563416.
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Affiliation(s)
- Mingjun Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Tao Yu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guanqun Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guangchao Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhuang Guo
- Department of Gastroenterology, Shengli Oilfield Central Hospital, Dongying, China
| | - Limei Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Qingqing Qi
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoxiao Yang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Junyan Qu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiao Wang
- Department of Pathology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
- *Correspondence: Yanqing Li,
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Sugano K, Spechler SJ, El-Omar EM, McColl KEL, Takubo K, Gotoda T, Fujishiro M, Iijima K, Inoue H, Kawai T, Kinoshita Y, Miwa H, Mukaisho KI, Murakami K, Seto Y, Tajiri H, Bhatia S, Choi MG, Fitzgerald RC, Fock KM, Goh KL, Ho KY, Mahachai V, O'Donovan M, Odze R, Peek R, Rugge M, Sharma P, Sollano JD, Vieth M, Wu J, Wu MS, Zou D, Kaminishi M, Malfertheiner P. Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction. Gut 2022; 71:1488-1514. [PMID: 35725291 PMCID: PMC9279854 DOI: 10.1136/gutjnl-2022-327281] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE An international meeting was organised to develop consensus on (1) the landmarks to define the gastro-oesophageal junction (GOJ), (2) the occurrence and pathophysiological significance of the cardiac gland, (3) the definition of the gastro-oesophageal junctional zone (GOJZ) and (4) the causes of inflammation, metaplasia and neoplasia occurring in the GOJZ. DESIGN Clinical questions relevant to the afore-mentioned major issues were drafted for which expert panels formulated relevant statements and textural explanations.A Delphi method using an anonymous system was employed to develop the consensus, the level of which was predefined as ≥80% of agreement. Two rounds of voting and amendments were completed before the meeting at which clinical questions and consensus were finalised. RESULTS Twenty eight clinical questions and statements were finalised after extensive amendments. Critical consensus was achieved: (1) definition for the GOJ, (2) definition of the GOJZ spanning 1 cm proximal and distal to the GOJ as defined by the end of palisade vessels was accepted based on the anatomical distribution of cardiac type gland, (3) chemical and bacterial (Helicobacter pylori) factors as the primary causes of inflammation, metaplasia and neoplasia occurring in the GOJZ, (4) a new definition of Barrett's oesophagus (BO). CONCLUSIONS This international consensus on the new definitions of BO, GOJ and the GOJZ will be instrumental in future studies aiming to resolve many issues on this important anatomic area and hopefully will lead to better classification and management of the diseases surrounding the GOJ.
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Affiliation(s)
- Kentaro Sugano
- Division of Gastroenterology, Department of Medicine, Jichi Medical University, Shimotsuke, Japan
| | - Stuart Jon Spechler
- Division of Gastroenterology, Center for Esophageal Diseases, Baylor University Medical Center, Dallas, Texas, USA
| | - Emad M El-Omar
- Microbiome Research Centre, St George & Sutherland Clinical Campuses, School of Clinical Medicine, Faculty of Medicine & Health, Sydney, New South Wales, Australia
| | - Kenneth E L McColl
- Division of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Takuji Gotoda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Katsunori Iijima
- Department of Gastroenterology, Akita University Graduate School of Medicine, Akita, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Takashi Kawai
- Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | | | - Hiroto Miwa
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo College of Medicine, Kobe, Japan
| | - Ken-ichi Mukaisho
- Education Center for Medicine and Nursing, Shiga University of Medical Science, Otsu, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Oita University Faculty of Medicine, Yuhu, Japan
| | - Yasuyuki Seto
- Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hisao Tajiri
- Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | | | - Myung-Gyu Choi
- Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, The Republic of Korea
| | - Rebecca C Fitzgerald
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge, UK
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Duke NUS School of Medicine, National University of Singapore, Singapore
| | | | - Khek Yu Ho
- Department of Medicine, National University of Singapore, Singapore
| | - Varocha Mahachai
- Center of Excellence in Digestive Diseases, Thammasat University and Science Resarch and Innovation, Bangkok, Thailand
| | - Maria O'Donovan
- Department of Histopathology, Cambridge University Hospital NHS Trust UK, Cambridge, UK
| | - Robert Odze
- Department of Pathology, Tuft University School of Medicine, Boston, Massachusetts, USA
| | - Richard Peek
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Massimo Rugge
- Department of Medicine DIMED, Surgical Pathology and Cytopathology Unit, University of Padova, Padova, Italy
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Jose D Sollano
- Department of Medicine, University of Santo Tomas, Manila, Philippines
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Friedrich-Alexander University Erlangen, Nurenberg, Germany
| | - Justin Wu
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Ming-Shiang Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Peter Malfertheiner
- Medizinixhe Klinik und Poliklinik II, Ludwig Maximillian University Klinikum, Munich, Germany,Klinik und Poliklinik für Radiologie, Ludwig Maximillian University Klinikum, Munich, Germany
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18
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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19
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Chen PC, Lu YR, Kang YN, Chang CC. The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study. J Med Internet Res 2022; 24:e27694. [PMID: 35576561 PMCID: PMC9152716 DOI: 10.2196/27694] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 10/23/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. OBJECTIVE The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. METHODS We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. RESULTS We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. CONCLUSIONS For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. TRIAL REGISTRATION PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.
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Affiliation(s)
- Pei-Chin Chen
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yun-Ru Lu
- Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yi-No Kang
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan.,Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.,Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
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Kutsumi H. Contribution of the Japan Gastroenterological Endoscopy Society to promote computer-aided diagnosis/detection system development using artificial intelligence technology. Dig Endosc 2022; 34 Suppl 2:132-135. [PMID: 34652003 DOI: 10.1111/den.14146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Hiromu Kutsumi
- Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan
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21
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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22
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Li J, Zhu Y, Dong Z, He X, Xu M, Liu J, Zhang M, Tao X, Du H, Chen D, Huang L, Shang R, Zhang L, Luo R, Zhou W, Deng Y, Huang X, Li Y, Chen B, Gong R, Zhang C, Li X, Wu L, Yu H. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study. EClinicalMedicine 2022; 46:101366. [PMID: 35521066 PMCID: PMC9061989 DOI: 10.1016/j.eclinm.2022.101366] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient survival. However, most previous computer-aided-diagnosis (CAD) systems did not concretize or explain diagnostic theories. We aimed to develop a logical anthropomorphic artificial intelligence (AI) diagnostic system named ENDOANGEL-LA (logical anthropomorphic) for EGCs under magnifying image enhanced endoscopy (M-IEE). METHODS We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared. FINDINGS Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model. INTERPRETATION We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs. FUNDING This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Nursing Department of Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, PR China
| | - Mengjiao Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Hongliu Du
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Di Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Renquan Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yunchao Deng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Rongrong Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Xun Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Correspondeing authors at: Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China.
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China
- Hubei Key Laboratory of Digestive System, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
- Correspondeing authors at: Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei 430060, PR China.
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23
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Abe S, Tomizawa Y, Saito Y. Can artificial intelligence be your angel to diagnose early gastric cancer in real clinical practice? Gastrointest Endosc 2022; 95:679-681. [PMID: 35177258 DOI: 10.1016/j.gie.2021.12.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 12/31/2021] [Indexed: 12/11/2022]
Affiliation(s)
- Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Yutaka Tomizawa
- Division of Gastroenterology, Harborview Medical Center, University of Washington, Seattle, Washington, USA
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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24
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Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y. Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc 2022; 95:599-609.e7. [PMID: 34979114 DOI: 10.1016/j.gie.2021.12.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists. METHODS PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis. RESULTS Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively. CONCLUSIONS The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.
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Affiliation(s)
- Fang Xie
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Keqiang Zhang
- Second Hospital of Jilin University, Changchun, Jilin, China
| | - Feng Li
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Guorong Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Ni
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Wei Zhang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Junchao Wang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, Jilin, China
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25
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Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc 2022; 95:671-678.e4. [PMID: 34896101 DOI: 10.1016/j.gie.2021.11.040] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/20/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning-based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE). METHODS M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice. RESULTS A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice. CONCLUSIONS This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.).
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26
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Oura H, Matsumura T, Fujie M, Ishikawa T, Nagashima A, Shiratori W, Tokunaga M, Kaneko T, Imai Y, Oike T, Yokoyama Y, Akizue N, Ota Y, Okimoto K, Arai M, Nakagawa Y, Inada M, Yamaguchi K, Kato J, Kato N. Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer. Gastric Cancer 2022; 25:392-400. [PMID: 34652556 DOI: 10.1007/s10120-021-01256-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND This study aimed to prevent missing gastric cancer and point out low-quality images by developing a double-check support system (DCSS) for esophagogastroduodenoscopy (EGD) still images using artificial intelligence. METHODS We extracted 12,977 still EGD images from 855 cases with cancer [821 with early gastric carcinoma (EGC) and 34 malignant lymphoma (ML)] and developed a lesion detection system using 10,994 images. The remaining images were used as a test dataset. Additional validation was performed using a new dataset containing 50 EGC and 1,200 non-GC images by comparing the interpretation of ten endoscopists (five trainees and five experts). Furthermore, we developed another system to detect low-quality images, which are not suitable for diagnosis, using 2198 images. RESULTS In the validation of 1983 images from the 124 cancer cases, the DCSS diagnosed cancer with a sensitivity of 89.2%, positive predictive value (PPV) of 93.3%, and an accuracy of 83.3%. EGC was detected in 93.2% and ML in 92.5% of cases. Comparing with the endoscopists, sensitivity was significantly higher in the DCSS, and the average diagnostic time was significantly shorter using the DCSS than that by the trainees. The sensitivity, specificity, PPV, and accuracy in detecting low-quality images were 65.8%, 93.1%, 79.6%, and 85.2% for "Blur" and 57.8%, 91.7%, 82.2%, and 78.1% for "Mucus adhesion," respectively. CONCLUSIONS The DCSS showed excellent capability in detecting lesions and pointing out low-quality images.
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Affiliation(s)
- Hirotaka Oura
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Tomoaki Matsumura
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan.
| | - Mai Fujie
- Department of Clinical Engineering Center, Chiba University Hospital, Chiba, Japan
| | - Tsubasa Ishikawa
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Ariki Nagashima
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Wataru Shiratori
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Mamoru Tokunaga
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Tatsuya Kaneko
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Yushi Imai
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Tsubasa Oike
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Yuya Yokoyama
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Naoki Akizue
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Yuki Ota
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Kenichiro Okimoto
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Makoto Arai
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Yuki Nakagawa
- Chiba Foundation for Health Promotion and Disease Prevention, Chiba, Japan
| | - Mari Inada
- Chiba Foundation for Health Promotion and Disease Prevention, Chiba, Japan
| | - Kazuya Yamaguchi
- Chiba Foundation for Health Promotion and Disease Prevention, Chiba, Japan
| | - Jun Kato
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
| | - Naoya Kato
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chiba, 260-8670, Japan
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Jin Z, Gan T, Wang P, Fu Z, Zhang C, Yan Q, Zheng X, Liang X, Ye X. Deep learning for gastroscopic images: computer-aided techniques for clinicians. Biomed Eng Online 2022; 21:12. [PMID: 35148764 PMCID: PMC8832738 DOI: 10.1186/s12938-022-00979-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
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Affiliation(s)
- Ziyi Jin
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Tianyuan Gan
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Peng Wang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Zuoming Fu
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Chongan Zhang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Qinglai Yan
- Hangzhou Center for Medical Device Quality Supervision and Testing, CFDA, Hangzhou, 310000, People's Republic of China
| | - Xueyong Zheng
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xiao Liang
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xuesong Ye
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.
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Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc 2022; 95:269-280.e6. [PMID: 34547254 DOI: 10.1016/j.gie.2021.09.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).
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Xiao Z, Ji D, Li F, Li Z, Bao Z. Application of Artificial Intelligence in Early Gastric Cancer Diagnosis. Digestion 2022; 103:69-75. [PMID: 34666330 DOI: 10.1159/000519601] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/13/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps. SUMMARY With AI's efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. Key messages: This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.
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Affiliation(s)
- Zili Xiao
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,
| | - Danian Ji
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Feng Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhengliang Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
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Wu L, Wang J, He X, Zhu Y, Jiang X, Chen Y, Wang Y, Huang L, Shang R, Dong Z, Chen B, Tao X, Wu Q, Yu H. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc 2022; 95:92-104.e3. [PMID: 34245752 DOI: 10.1016/j.gie.2021.06.033] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND AND AIMS We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition. METHODS This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs. RESULTS One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively). CONCLUSIONS The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yiyun Chen
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qi Wu
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:cancers13215494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Gastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital tool for the management of these cancer patients. Abstract Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Islam MM, Poly TN, Walther BA, Lin MC, Li YC(J. Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network. Cancers (Basel) 2021; 13:cancers13215253. [PMID: 34771416 PMCID: PMC8582393 DOI: 10.3390/cancers13215253] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 02/08/2023] Open
Abstract
Simple Summary Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Previous studies reported that the detection rate of gastric cancer (EGC) at an earlier stage is low, and the overall false-negative rate with esophagogastroduodenoscopy (EGD) is up to 25.8%, which often leads to inappropriate treatment. Accurate diagnosis of EGC can reduce unnecessary interventions and benefits treatment planning. Convolutional neural network (CNN) models have recently shown promising performance in analyzing medical images, including endoscopy. This study shows that an automated tool based on the CNN model could improve EGC diagnosis and treatment decision. Abstract Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current literature to evaluate the performance of the CNN model in detecting EGC. We conducted a systematic search in the online databases (e.g., PubMed, Embase, and Web of Science) for all relevant original studies on the subject of CNN in EGC published between 1 January 2010, and 26 March 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Moreover, a summary receiver operating characteristic curve (SROC) was plotted. Of the 171 studies retrieved, 15 studies met inclusion criteria. The application of the CNN model in the diagnosis of EGC achieved a SROC of 0.95, with corresponding sensitivity of 0.89 (0.88–0.89), and specificity of 0.89 (0.89–0.90). Pooled sensitivity and specificity for experts endoscopists were 0.77 (0.76–0.78), and 0.92 (0.91–0.93), respectively. However, the overall SROC for the CNN model and expert endoscopists was 0.95 and 0.90. The findings of this comprehensive study show that CNN model exhibited comparable performance to endoscopists in the diagnosis of EGC using digital endoscopy images. Given its scalability, the CNN model could enhance the performance of endoscopists to correctly stratify EGC patients and reduce work load.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Bruno Andreas Walther
- Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany;
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (M.-C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Correspondence: ; Tel.: +886-2-27361661 (ext. 7600); Fax: +886-2-6638-75371
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Huang B, Tian S, Zhan N, Ma J, Huang Z, Zhang C, Zhang H, Ming F, Liao F, Ji M, Zhang J, Liu Y, He P, Deng B, Hu J, Dong W. Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study. EBioMedicine 2021; 73:103631. [PMID: 34678610 PMCID: PMC8529077 DOI: 10.1016/j.ebiom.2021.103631] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 10/03/2021] [Accepted: 10/04/2021] [Indexed: 02/07/2023] Open
Abstract
Background To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. Methods 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. Findings The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). Interpretation Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. Funding Not applicable.
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Affiliation(s)
- Binglu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Shan Tian
- Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Na Zhan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Jingjing Ma
- Department of Geriatrics, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | | | | | - Hao Zhang
- Ankon Technologies Co., Ltd, Wuhan, China
| | | | - Fei Liao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Mengyao Ji
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Jixiang Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Yinghui Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Pengzhan He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Beiying Deng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Jiaming Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, 430060, China.
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Zhou J, Hu N, Huang ZY, Song B, Wu CC, Zeng FX, Wu M. Application of artificial intelligence in gastrointestinal disease: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1188. [PMID: 34430629 PMCID: PMC8350704 DOI: 10.21037/atm-21-3001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 06/29/2021] [Indexed: 02/05/2023]
Abstract
Objective We collected evidence on the application of artificial intelligence (AI) in gastroenterology field. The review was carried out from two aspects of endoscopic types and gastrointestinal diseases, and briefly summarized the challenges and future directions in this field. Background Due to the advancement of computational power and a surge of available data, a solid foundation has been laid for the growth of AI. Specifically, varied machine learning (ML) techniques have been emerging in endoscopic image analysis. To improve the accuracy and efficiency of clinicians, AI has been widely applied to gastrointestinal endoscopy. Methods PubMed electronic database was searched using the keywords containing “AI”, “ML”, “deep learning (DL)”, “convolution neural network”, “endoscopy (such as white light endoscopy (WLE), narrow band imaging (NBI) endoscopy, magnifying endoscopy with narrow band imaging (ME-NBI), chromoendoscopy, endocytoscopy (EC), and capsule endoscopy (CE))”. Search results were assessed for relevance and then used for detailed discussion. Conclusions This review described the basic knowledge of AI, ML, and DL, and summarizes the application of AI in various endoscopes and gastrointestinal diseases. Finally, the challenges and directions of AI in clinical application were discussed. At present, the application of AI has solved some clinical problems, but more still needs to be done.
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Affiliation(s)
- Jun Zhou
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, China
| | - Na Hu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zhi-Yin Huang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Chun-Cheng Wu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Fan-Xin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, China
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36
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Tokat M, van Tilburg L, Koch AD, Spaander MCW. Artificial Intelligence in Upper Gastrointestinal Endoscopy. Dig Dis 2021; 40:395-408. [PMID: 34348267 DOI: 10.1159/000518232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/23/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Over the past decade, several artificial intelligence (AI) systems are developed to assist in endoscopic assessment of (pre-)cancerous lesions of the gastrointestinal (GI) tract. In this review, we aimed to provide an overview of the possible indications of AI technology in upper GI endoscopy and hypothesize about potential challenges for its use in clinical practice. SUMMARY Application of AI in upper GI endoscopy has been investigated for several indications: (1) detection, characterization, and delineation of esophageal and gastric cancer (GC) and their premalignant conditions; (2) prediction of tumor invasion; and (3) detection of Helicobacter pylori. AI systems show promising results with an accuracy of up to 99% for the detection of superficial and advanced upper GI cancers. AI outperformed trainee and experienced endoscopists for the detection of esophageal lesions and atrophic gastritis. For GC, AI outperformed mid-level and trainee endoscopists but not expert endoscopists. KEY MESSAGES Application of artificial intelligence (AI) in upper gastrointestinal endoscopy may improve early diagnosis of esophageal and gastric cancer and may enable endoscopists to better identify patients eligible for endoscopic resection. The benefit of AI on the quality of upper endoscopy still needs to be demonstrated, while prospective trials are needed to confirm accuracy and feasibility during real-time daily endoscopy.
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Affiliation(s)
- Meltem Tokat
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Laurelle van Tilburg
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Arjun D Koch
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Abstract
This article explores advances in endoscopic neoplasia detection with supporting clinical evidence and future aims. The ability to detect early gastric neoplastic lesions amenable to curative endoscopic submucosal dissection provides the opportunity to decrease gastric cancer mortality rates. Newer imaging techniques offer enhanced views of mucosal and microvascular structures and show promise in differentiating benign from malignant lesions and improving targeted biopsies. Conventional chromoendoscopy is well studied and validated. Narrow band imaging demonstrates superiority over magnified white light. Autofluorescence imaging, i-scan, flexible spectral imaging color enhancement, and bright image enhanced endoscopy show promise but insufficient evidence to change current clinical practice.
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Affiliation(s)
- Andrew Canakis
- Department of Medicine, Boston University School of Medicine, Boston Medical Center, 72 East Concord Street, Evans 124, Boston, MA 02118, USA. https://twitter.com/AndrewCanakis
| | - Raymond Kim
- Division of Gastroenterology & Hepatology, University of Maryland Medical Center, University of Maryland School of Medicine, 22 South Greene Street, Baltimore, MD 21201, USA.
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38
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Kim JH, Nam SJ, Park SC. Usefulness of artificial intelligence in gastric neoplasms. World J Gastroenterol 2021; 27:3543-3555. [PMID: 34239268 PMCID: PMC8240061 DOI: 10.3748/wjg.v27.i24.3543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
Recently, studies in many medical fields have reported that image analysis based on artificial intelligence (AI) can be used to analyze structures or features that are difficult to identify with human eyes. To diagnose early gastric cancer, related efforts such as narrow-band imaging technology are on-going. However, diagnosis is often difficult. Therefore, a diagnostic method based on AI for endoscopic imaging was developed and its effectiveness was confirmed in many studies. The gastric cancer diagnostic program based on AI showed relatively high diagnostic accuracy and could differentially diagnose non-neoplastic lesions including benign gastric ulcers and dysplasia. An AI system has also been developed that helps to predict the invasion depth of gastric cancer through endoscopic images and observe the stomach during endoscopy without blind spots. Therefore, if AI is used in the field of endoscopy, it is expected to aid in the diagnosis of gastric neoplasms and determine the application of endoscopic therapy by predicting the invasion depth.
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Affiliation(s)
- Ji Hyun Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
| | - Seung-Joo Nam
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
| | - Sung Chul Park
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
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Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27:2979-2993. [PMID: 34168402 PMCID: PMC8192292 DOI: 10.3748/wjg.v27.i22.2979] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.
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Affiliation(s)
- Yu-Jer Hsiao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yuan-Chih Wen
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Medical Education, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Wei-Yi Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ying Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Internal Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Critical Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Yueh Chien
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | | | - De-Kuang Hwang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Tai-Chi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Yun-Chia Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Ting-Yi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Kao-Jung Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Hwa Chiou
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Ying-Chun Jheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Big Data Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
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Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Early diagnosis and timely treatment are crucial in reducing cancer-related mortality. Artificial intelligence (AI) has greatly relieved clinical workloads and changed the current medical workflows. We searched for recent studies, reports and reviews referring to AI and solid tumors; many reviews have summarized AI applications in the diagnosis and treatment of a single tumor type. We herein systematically review the advances of AI application in multiple solid tumors including esophagus, stomach, intestine, breast, thyroid, prostate, lung, liver, cervix, pancreas and kidney with a specific focus on the continual improvement on model performance in imaging practice.
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Affiliation(s)
- Ying Shao
- Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
| | - Yu-Xuan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Huan-Huan Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi-Chang Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jie-Xin Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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Jiang K, Jiang X, Pan J, Wen Y, Huang Y, Weng S, Lan S, Nie K, Zheng Z, Ji S, Liu P, Li P, Liu F. Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis. Front Med (Lausanne) 2021; 8:629080. [PMID: 33791323 PMCID: PMC8005567 DOI: 10.3389/fmed.2021.629080] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Background & Aims: Gastric cancer is the common malignancies from cancer worldwide. Endoscopy is currently the most effective method to detect early gastric cancer (EGC). However, endoscopy is not infallible and EGC can be missed during endoscopy. Artificial intelligence (AI)-assisted endoscopic diagnosis is a recent hot spot of research. We aimed to quantify the diagnostic value of AI-assisted endoscopy in diagnosing EGC. Method: The PubMed, MEDLINE, Embase and the Cochrane Library Databases were searched for articles on AI-assisted endoscopy application in EGC diagnosis. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated, and the endoscopists' diagnostic value was evaluated for comparison. The subgroup was set according to endoscopy modality, and number of training images. A funnel plot was delineated to estimate the publication bias. Result: 16 studies were included in this study. We indicated that the application of AI in endoscopic detection of EGC achieved an AUC of 0.96 (95% CI, 0.94–0.97), a sensitivity of 86% (95% CI, 77–92%), and a specificity of 93% (95% CI, 89–96%). In AI-assisted EGC depth diagnosis, the AUC was 0.82(95% CI, 0.78–0.85), and the pooled sensitivity and specificity was 0.72(95% CI, 0.58–0.82) and 0.79(95% CI, 0.56–0.92). The funnel plot showed no publication bias. Conclusion: The AI applications for EGC diagnosis seemed to be more accurate than the endoscopists. AI assisted EGC diagnosis was more accurate than experts. More prospective studies are needed to make AI-aided EGC diagnosis universal in clinical practice.
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Affiliation(s)
- Kailin Jiang
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaotao Jiang
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jinglin Pan
- Department of Spleen-Stomach and Liver Diseases, Traditional Chinese Medicine Hospital of Hainan Province Affiliated to Guangzhou University of Chinese Medicine, Haikou, China
| | - Yi Wen
- Department of Gastroenterology, First Affiliation Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yuanchen Huang
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Senhui Weng
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shaoyang Lan
- Department of Gastroenterology, First Affiliation Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kechao Nie
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhihua Zheng
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shuling Ji
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Peng Liu
- First College of Clinic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Peiwu Li
- Department of Gastroenterology, First Affiliation Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fengbin Liu
- Department of Gastroenterology, First Affiliation Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
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Sutton RA, Sharma P. Overcoming barriers to implementation of artificial intelligence in gastroenterology. Best Pract Res Clin Gastroenterol 2021; 52-53:101732. [PMID: 34172254 DOI: 10.1016/j.bpg.2021.101732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Artificial intelligence is poised to revolutionize the field of medicine, however significant questions must be answered prior to its implementation on a regular basis. Many artificial intelligence algorithms remain limited by isolated datasets which may cause selection bias and truncated learning for the program. While a central database may solve this issue, several barriers such as security, patient consent, and management structure prevent this from being implemented. An additional barrier to daily use is device approval by the Food and Drug Administration. In order for this to occur, clinical studies must address new endpoints, including and beyond the traditional bio- and medical statistics. These must showcase artificial intelligence's benefit and answer key questions, including challenges posed in the field of medical ethics.
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Affiliation(s)
- Richard A Sutton
- University of Kansas Medical Center 3901 Rainbow Blvd, Kansas City, KS, USA; Kansas City Veteran's Affairs Medical Center 4801 Linwood Blvd, Kansas City, MO, USA.
| | - Prateek Sharma
- University of Kansas Medical Center 3901 Rainbow Blvd, Kansas City, KS, USA; Kansas City Veteran's Affairs Medical Center 4801 Linwood Blvd, Kansas City, MO, USA.
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Hirasawa T, Ikenoyama Y, Ishioka M, Namikawa K, Horiuchi Y, Nakashima H, Fujisaki J. Current status and future perspective of artificial intelligence applications in endoscopic diagnosis and management of gastric cancer. Dig Endosc 2021; 33:263-272. [PMID: 33159692 DOI: 10.1111/den.13890] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/27/2020] [Accepted: 11/03/2020] [Indexed: 01/07/2023]
Abstract
Image recognition using artificial intelligence (AI) has progressed significantly due to innovative technologies such as machine learning and deep learning. In the field of gastric cancer (GC) management, research on AI-based diagnosis such as anatomical classification of endoscopic images, diagnosis of Helicobacter pylori infection, and detection and qualitative diagnosis of GC is being conducted, and an accuracy equivalent to that of physicians has been reported. It is expected that AI will soon be introduced in the field of endoscopic diagnosis and management of gastric cancer as a supportive tool for physicians, thus improving the quality of medical care.
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Affiliation(s)
- Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yohei Ikenoyama
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Mitsuaki Ishioka
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Ken Namikawa
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | | | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
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Sumiyama K, Futakuchi T, Kamba S, Matsui H, Tamai N. Artificial intelligence in endoscopy: Present and future perspectives. Dig Endosc 2021; 33:218-230. [PMID: 32935376 DOI: 10.1111/den.13837] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/04/2020] [Indexed: 02/08/2023]
Abstract
Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL-based models to real-time computer-aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL-based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles.
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Affiliation(s)
- Kazuki Sumiyama
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Toshiki Futakuchi
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Shunsuke Kamba
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Hiroaki Matsui
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Naoto Tamai
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
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45
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Suzuki H, Yoshitaka T, Yoshio T, Tada T. Artificial intelligence for cancer detection of the upper gastrointestinal tract. Dig Endosc 2021; 33:254-262. [PMID: 33222330 DOI: 10.1111/den.13897] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/16/2020] [Indexed: 12/20/2022]
Abstract
In recent years, artificial intelligence (AI) has been found to be useful to physicians in the field of image recognition due to three elements: deep learning (that is, CNN, convolutional neural network), a high-performance computer, and a large amount of digitized data. In the field of gastrointestinal endoscopy, Japanese endoscopists have produced the world's first achievements of CNN-based AI system for detecting gastric and esophageal cancers. This study reviews papers on CNN-based AI for gastrointestinal cancers, and discusses the future of this technology in clinical practice. Employing AI-based endoscopes would enable early cancer detection. The better diagnostic abilities of AI technology may be beneficial in early gastrointestinal cancers in which endoscopists have variable diagnostic abilities and accuracy. AI coupled with the expertise of endoscopists would increase the accuracy of endoscopic diagnosis.
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Affiliation(s)
- Hideo Suzuki
- Department of Gastroenterology, Graduate School of Institute Clinical Medicine, University of Tsukuba, Ibaraki, Japan
| | - Tokai Yoshitaka
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiyuki Yoshio
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,AI Medical Service Inc., Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
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Arribas J, Antonelli G, Frazzoni L, Fuccio L, Ebigbo A, van der Sommen F, Ghatwary N, Palm C, Coimbra M, Renna F, Bergman JJGHM, Sharma P, Messmann H, Hassan C, Dinis-Ribeiro MJ. Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut 2020; 70:gutjnl-2020-321922. [PMID: 33127833 DOI: 10.1136/gutjnl-2020-321922] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/18/2020] [Accepted: 09/20/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. DESIGN We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. RESULTS Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. CONCLUSION We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.
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Affiliation(s)
- Julia Arribas
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Giulio Antonelli
- Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Leonardo Frazzoni
- Department of Medical and Surgical Sciences, S.Orsola-Malpighi Hospital, University of Bologna, Bologna, BO, Italy
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences, S.Orsola-Malpighi Hospital, University of Bologna, Bologna, BO, Italy
| | - Alanna Ebigbo
- III Medizinische Klinik, UniversitatsKlinikum Augsburg, Augsburg, Germany
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Noha Ghatwary
- Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria, Egypt
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
- Regensburg Center of Health Sciences and Technology (RCHST), OTH Regensburg, Regensburg, Germany
| | - Miguel Coimbra
- INESC TEC, Faculdade de Ciências, University of Porto, Porto, Portugal
| | - Francesco Renna
- Instituto de Telecomunicações, Faculdade de Ciencias, University of Porto, Porto, Portugal
| | - J J G H M Bergman
- Dept of Gastroenterology, Academic Medical Center, Amsterdam, The Netherlands
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Helmut Messmann
- III Medizinische Klinik, UniversitatsKlinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
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Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020; 146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 06/26/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer. METHODS A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications. RESULTS A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance. CONCLUSIONS In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.
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Affiliation(s)
- Peng Jin
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaoyan Ji
- Department of Emergency Ward, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Wenzhe Kang
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hao Liu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fuhai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Haitao Hu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Weikun Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yantao Tian
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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