Artificial intelligence in gastrointestinal endoscopy
| Autor: | Rabindra R. Watson, Mansour A. Parsi, Shelby Sullivan, Allison R. Schulman, Guru Trikudanathan, David R. Lichtenstein, Rahul Pannala, John T. Maple, Arvind J. Trindade, Joshua Melson, Kumar Krishnan |
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| Rok vydání: | 2020 |
| Předmět: |
CNN
convolutional neural network ADR adenoma detection rate AMR adenoma miss rate Convolutional neural network law.invention 03 medical and health sciences 0302 clinical medicine Capsule endoscopy law CADx CAD studies for colon polyp classification BE Barrett’s esophagus VLE volumetric laser endomicroscopy WCE wireless capsule endoscopy Medicine Radiology Nuclear Medicine and imaging ML machine learning Gastrointestinal endoscopy SVM support vector machine PIVI preservation and Incorporation of Valuable Endoscopic Innovations DL deep learning Contextual image classification Artificial neural network business.industry Deep learning Gastroenterology NBI narrow-band imaging WL white light CAD computer-aided diagnosis ANN artificial neural network HDWL high-definition white light medicine.disease CI confidence interval GI gastroenterology ASGE society document NPV negative predictive value Computer-aided diagnosis 030220 oncology & carcinogenesis Barrett's esophagus CRC colorectal cancer HD-WLE high-definition white light endoscopy AI artificial intelligence 030211 gastroenterology & hepatology CADe CAD studies for colon polyp detection Artificial intelligence business |
| Zdroj: | VideoGIE |
| ISSN: | 2468-4481 |
| DOI: | 10.1016/j.vgie.2020.08.013 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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