Artificial intelligence in gastrointestinal endoscopy
Background and Aims: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect i...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | VideoGIE |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468448120302721 |
| _version_ | 1827877507162963968 |
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| author | Rahul Pannala, MD, MPH, FASGE Kumar Krishnan, MD Joshua Melson, MD, MPH, FASGE Mansour A. Parsi, MD, MPH, FASGE Allison R. Schulman, MD, MPH Shelby Sullivan, MD Guru Trikudanathan, MBBS Arvind J. Trindade, MD Rabindra R. Watson, MD John T. Maple, DO, FASGE David R. Lichtenstein, MD, FASGE |
| author_facet | Rahul Pannala, MD, MPH, FASGE Kumar Krishnan, MD Joshua Melson, MD, MPH, FASGE Mansour A. Parsi, MD, MPH, FASGE Allison R. Schulman, MD, MPH Shelby Sullivan, MD Guru Trikudanathan, MBBS Arvind J. Trindade, MD Rabindra R. Watson, MD John T. Maple, DO, FASGE David R. Lichtenstein, MD, FASGE |
| author_sort | Rahul Pannala, MD, MPH, FASGE |
| collection | DOAJ |
| description | Background and Aims: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. Methods: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Results: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images. Conclusions: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods. |
| first_indexed | 2024-03-12T17:38:30Z |
| format | Article |
| id | doaj.art-377b1eae945d44f7a9d7bdceedea9dab |
| institution | Directory Open Access Journal |
| issn | 2468-4481 |
| language | English |
| last_indexed | 2024-03-12T17:38:30Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | VideoGIE |
| spelling | doaj.art-377b1eae945d44f7a9d7bdceedea9dab2023-08-04T05:50:47ZengElsevierVideoGIE2468-44812020-12-01512598613Artificial intelligence in gastrointestinal endoscopyRahul Pannala, MD, MPH, FASGE0Kumar Krishnan, MD1Joshua Melson, MD, MPH, FASGE2Mansour A. Parsi, MD, MPH, FASGE3Allison R. Schulman, MD, MPH4Shelby Sullivan, MD5Guru Trikudanathan, MBBS6Arvind J. Trindade, MD7Rabindra R. Watson, MD8John T. Maple, DO, FASGE9David R. Lichtenstein, MD, FASGE10Department of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, ArizonaDivision of Gastroenterology, Department of Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MassachusettsDivision of Digestive Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, IllinoisSection for Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, LouisianaDepartment of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, MichiganDivision of Gastroenterology and Hepatology, University of Colorado School of Medicine, Aurora, ColoradoDepartment of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, MinnesotaDepartment of Gastroenterology, Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical Center, New Hyde Park, New YorkDepartment of Gastroenterology, Interventional Endoscopy Services, California Pacific Medical Center, San Francisco, CaliforniaDivision of Digestive Diseases and Nutrition, University of Oklahoma Health Sciences Center, Oklahoma City, OklahomaDivision of Gastroenterology, Boston Medical Center, Boston University School of Medicine, Boston, MassachusettsBackground and Aims: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. Methods: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Results: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images. Conclusions: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.http://www.sciencedirect.com/science/article/pii/S2468448120302721 |
| spellingShingle | Rahul Pannala, MD, MPH, FASGE Kumar Krishnan, MD Joshua Melson, MD, MPH, FASGE Mansour A. Parsi, MD, MPH, FASGE Allison R. Schulman, MD, MPH Shelby Sullivan, MD Guru Trikudanathan, MBBS Arvind J. Trindade, MD Rabindra R. Watson, MD John T. Maple, DO, FASGE David R. Lichtenstein, MD, FASGE Artificial intelligence in gastrointestinal endoscopy VideoGIE |
| title | Artificial intelligence in gastrointestinal endoscopy |
| title_full | Artificial intelligence in gastrointestinal endoscopy |
| title_fullStr | Artificial intelligence in gastrointestinal endoscopy |
| title_full_unstemmed | Artificial intelligence in gastrointestinal endoscopy |
| title_short | Artificial intelligence in gastrointestinal endoscopy |
| title_sort | artificial intelligence in gastrointestinal endoscopy |
| url | http://www.sciencedirect.com/science/article/pii/S2468448120302721 |
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