Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging
Background and Aim Magnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep l...
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| Veröffentlicht in: | Journal of gastroenterology and hepatology Jg. 36; H. 2; S. 482 - 489 |
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| Hauptverfasser: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Australia
Wiley Subscription Services, Inc
01.02.2021
John Wiley and Sons Inc |
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| ISSN: | 0815-9319, 1440-1746, 1440-1746 |
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| Abstract | Background and Aim
Magnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI‐assisted CNN computer‐aided diagnosis (CAD) system, based on ME‐NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI‐assisted CNN‐CAD system.
Methods
The AI‐assisted CNN‐CAD system (ResNet50) was trained and validated on a dataset of 5574 ME‐NBI images (3797 EGCs, 1777 non‐cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME‐NBI images (1430 EGCs, 870 non‐cancerous mucosa and lesions) was assessed using the AI‐assisted CNN‐CAD system.
Results
The AI‐assisted CNN‐CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low‐quality or of superficially depressed and intestinal‐type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.
Conclusions
The AI‐assisted CNN‐CAD system for ME‐NBI diagnosis of EGC could process many stored ME‐NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME‐NBI diagnosis of EGC in practice. |
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| AbstractList | Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system.BACKGROUND AND AIMMagnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system.The AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system.METHODSThe AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system.The AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.RESULTSThe AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.The AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice.CONCLUSIONSThe AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice. Background and Aim Magnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI‐assisted CNN computer‐aided diagnosis (CAD) system, based on ME‐NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI‐assisted CNN‐CAD system. Methods The AI‐assisted CNN‐CAD system (ResNet50) was trained and validated on a dataset of 5574 ME‐NBI images (3797 EGCs, 1777 non‐cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME‐NBI images (1430 EGCs, 870 non‐cancerous mucosa and lesions) was assessed using the AI‐assisted CNN‐CAD system. Results The AI‐assisted CNN‐CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low‐quality or of superficially depressed and intestinal‐type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists. Conclusions The AI‐assisted CNN‐CAD system for ME‐NBI diagnosis of EGC could process many stored ME‐NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME‐NBI diagnosis of EGC in practice. Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system. The AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system. The AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists. The AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice. Background and AimMagnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI‐assisted CNN computer‐aided diagnosis (CAD) system, based on ME‐NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI‐assisted CNN‐CAD system.MethodsThe AI‐assisted CNN‐CAD system (ResNet50) was trained and validated on a dataset of 5574 ME‐NBI images (3797 EGCs, 1777 non‐cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME‐NBI images (1430 EGCs, 870 non‐cancerous mucosa and lesions) was assessed using the AI‐assisted CNN‐CAD system.ResultsThe AI‐assisted CNN‐CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low‐quality or of superficially depressed and intestinal‐type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.ConclusionsThe AI‐assisted CNN‐CAD system for ME‐NBI diagnosis of EGC could process many stored ME‐NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME‐NBI diagnosis of EGC in practice. |
| Author | Ueda, Kumiko Takeda, Tsutomu Nagahara, Akihito Hojo, Mariko Tada, Tomohiro Akazawa, Yoichi Matsumoto, Kenshi Yatagai, Noboru Matsumoto, Kohei Yao, Takashi Ueyama, Hiroya Kato, Yusuke Komori, Hiroyuki |
| AuthorAffiliation | 3 Department of Human Pathology Juntendo University School of Medicine Tokyo Japan 1 Department of Gastroenterology Juntendo University School of Medicine Tokyo Japan 2 AI Medical Service Inc. Tokyo Japan 4 Tada Tomohiro Institute of Gastroenterology and Proctology Saitama Japan |
| AuthorAffiliation_xml | – name: 4 Tada Tomohiro Institute of Gastroenterology and Proctology Saitama Japan – name: 1 Department of Gastroenterology Juntendo University School of Medicine Tokyo Japan – name: 3 Department of Human Pathology Juntendo University School of Medicine Tokyo Japan – name: 2 AI Medical Service Inc. Tokyo Japan |
| Author_xml | – sequence: 1 givenname: Hiroya orcidid: 0000-0002-5370-1009 surname: Ueyama fullname: Ueyama, Hiroya email: psyro@juntendo.ac.jp organization: Juntendo University School of Medicine – sequence: 2 givenname: Yusuke surname: Kato fullname: Kato, Yusuke organization: AI Medical Service Inc – sequence: 3 givenname: Yoichi surname: Akazawa fullname: Akazawa, Yoichi organization: Juntendo University School of Medicine – sequence: 4 givenname: Noboru surname: Yatagai fullname: Yatagai, Noboru organization: Juntendo University School of Medicine – sequence: 5 givenname: Hiroyuki surname: Komori fullname: Komori, Hiroyuki organization: Juntendo University School of Medicine – sequence: 6 givenname: Tsutomu surname: Takeda fullname: Takeda, Tsutomu organization: Juntendo University School of Medicine – sequence: 7 givenname: Kohei surname: Matsumoto fullname: Matsumoto, Kohei organization: Juntendo University School of Medicine – sequence: 8 givenname: Kumiko surname: Ueda fullname: Ueda, Kumiko organization: Juntendo University School of Medicine – sequence: 9 givenname: Kenshi surname: Matsumoto fullname: Matsumoto, Kenshi organization: Juntendo University School of Medicine – sequence: 10 givenname: Mariko surname: Hojo fullname: Hojo, Mariko organization: Juntendo University School of Medicine – sequence: 11 givenname: Takashi surname: Yao fullname: Yao, Takashi organization: Juntendo University School of Medicine – sequence: 12 givenname: Akihito surname: Nagahara fullname: Nagahara, Akihito organization: Juntendo University School of Medicine – sequence: 13 givenname: Tomohiro surname: Tada fullname: Tada, Tomohiro organization: Tada Tomohiro Institute of Gastroenterology and Proctology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32681536$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 The Authors. Journal of Gastroenterology and Hepatology published by Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd 2020 The Authors. Journal of Gastroenterology and Hepatology published by Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd. 2020. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | magnifying endoscopy convolutional neural network artificial intelligence narrow-band imaging early gastric cancer |
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| Notes | Author contribution Ueyama H, Nagahara A, and Tada T conceived and designed the study and wrote, edited, and reviewed the manuscript. Yao T performed all histopathological diagnosis. Akazawa Y, Yatagai N, Komori H, Takeda T, Matsumoto K, Ueda K, Matsumoto K, and Hojo M gathered ME‐NBI images and patients' clinical information. Kato Y provided valuable advice regarding the technical information and managed the AI‐assisted CNN‐CAD system and analyzed the data in this manuscript. All authors gave final approval for publication. Ueyama H, Nagahara A, and Tada T take full responsibility for the work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript. No author has a financial relationship relevant to this publication. Aoyama K and Kato Y are technical staff of AI Medical Service. Tada T is a shareholder of AI Medical Service. Declaration of conflict of interest ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Declaration of conflict of interest: No author has a financial relationship relevant to this publication. Aoyama K and Kato Y are technical staff of AI Medical Service. Tada T is a shareholder of AI Medical Service. Author contribution: Ueyama H, Nagahara A, and Tada T conceived and designed the study and wrote, edited, and reviewed the manuscript. Yao T performed all histopathological diagnosis. Akazawa Y, Yatagai N, Komori H, Takeda T, Matsumoto K, Ueda K, Matsumoto K, and Hojo M gathered ME‐NBI images and patients' clinical information. Kato Y provided valuable advice regarding the technical information and managed the AI‐assisted CNN‐CAD system and analyzed the data in this manuscript. All authors gave final approval for publication. Ueyama H, Nagahara A, and Tada T take full responsibility for the work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript. |
| ORCID | 0000-0002-5370-1009 |
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| PublicationDate | February 2021 |
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| PublicationTitle | Journal of gastroenterology and hepatology |
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Magnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI... Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early... Background and AimMagnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI... |
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| SubjectTerms | Accuracy Artificial intelligence convolutional neural network Deep learning Diagnosis early gastric cancer Endoscopy Gastric cancer Gastritis Intestine magnifying endoscopy Mucosa narrow‐band imaging Neural networks Regular |
| Title | Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging |
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