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Chen G, Shen J. Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease. Front Bioeng Biotechnol 2021; 9:635764. [PMID: 34307315 PMCID: PMC8297505 DOI: 10.3389/fbioe.2021.635764] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/09/2021] [Indexed: 12/18/2022] Open
Abstract
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.
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Affiliation(s)
- Guihua Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Shen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol 2021; 55:110-120. [PMID: 32925304 DOI: 10.1097/mcg.0000000000001423] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.
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Mao WB, Lyu JY, Vaishnani DK, Lyu YM, Gong W, Xue XL, Shentu YP, Ma J. Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors. World J Clin Cases 2020; 8:3971-3977. [PMID: 33024753 PMCID: PMC7520792 DOI: 10.12998/wjcc.v8.i18.3971] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/10/2020] [Accepted: 06/28/2020] [Indexed: 02/05/2023] Open
Abstract
As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.
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Affiliation(s)
- Wei-Bo Mao
- Department of Pathology, Lishui Hospital of Zhejiang University, Lishui Central Hospital, Lishui 323000, Zhejiang Province, China
| | - Jia-Yu Lyu
- Department of Psychiatry, The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China
| | - Deep K Vaishnani
- School of International Studies, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Yu-Man Lyu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Wei Gong
- Department of Pathology, Lishui Hospital of Zhejiang University, Lishui Central Hospital, Lishui 323000, Zhejiang Province, China
| | - Xi-Ling Xue
- Department of Psychiatry, The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China
| | - Yang-Ping Shentu
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
| | - Jun Ma
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
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Shung DL, Byrne MF. How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening. Gastrointest Endosc Clin N Am 2020; 30:585-595. [PMID: 32439090 DOI: 10.1016/j.giec.2020.02.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.
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Affiliation(s)
- Dennis L Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, P.O. Box 208019, New Haven, CT 06520-8019, USA
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, 5153 - 2775 Laurel Street, Vancouver, British Columbia, Canada.
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Nardone OM, Cannatelli R, Zardo D, Ghosh S, Iacucci M. Can advanced endoscopic techniques for assessment of mucosal inflammation and healing approximate histology in inflammatory bowel disease? Therap Adv Gastroenterol 2019; 12:1756284819863015. [PMID: 31360224 PMCID: PMC6640057 DOI: 10.1177/1756284819863015] [Citation(s) in RCA: 20] [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: 04/08/2019] [Accepted: 06/14/2019] [Indexed: 02/04/2023] Open
Abstract
The targets of therapy in inflammatory bowel disease have transformed in the last few years. The standard definition of mucosal healing assessed using white light standard definition endoscopy is being challenged because even when endoscopy suggests mucosal healing, the presence of histological activity can often still be observed. Of note, microscopic signs of inflammation correlate with clinical outcomes such as risk of relapse, hospitalization and colorectal cancer. Therefore, histological healing has increasingly become an important target to achieve. Advanced endoscopic technologies have been developed and many are starting to be adopted in daily clinical practice. They can provide a more detailed view of the mucosal and vascular architecture almost at the histology level, including crypt, vessel architecture and cellular infiltration. So, these can provide a more accurate definition of mucosal and histological healing. In this review we focus on new advanced endoscopic techniques, and how these have the potential to reduce the gap between histological and mucosal healing.
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Affiliation(s)
- Olga Maria Nardone
- Institute of Translational Medicine and
Institute of Immunology and Immunotherapy, University of Birmingham,
Heritage Building, Mindelsohn Way, Birmingham, B15 2TH, UK
| | - Rosanna Cannatelli
- Institute of Translational Medicine and
Institute of Immunology and Immunotherapy, University of Birmingham,
Birmingham, UK
| | - Davide Zardo
- University Hospitals Birmingham NHS Foundation
Trust, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Subrata Ghosh
- Institute of Translational Medicine and
Institute of Immunology and Immunotherapy, University of Birmingham,
Birmingham, UK
- University Hospitals Birmingham NHS Foundation
Trust, Queen Elizabeth Hospital Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre, University of
Birmingham and University Hospitals NHS Foundation Trust Birmingham,
UK
| | - Marietta Iacucci
- Institute of Translational Medicine and
Institute of Immunology and Immunotherapy, University of Birmingham,
Birmingham, UK
- University Hospitals Birmingham NHS Foundation
Trust, Queen Elizabeth Hospital Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre, University of
Birmingham and University Hospitals NHS Foundation Trust Birmingham,
UK
- University of Calgary, Calgary, Canada
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Cummins G, Cox BF, Ciuti G, Anbarasan T, Desmulliez MPY, Cochran S, Steele R, Plevris JN, Koulaouzidis A. Gastrointestinal diagnosis using non-white light imaging capsule endoscopy. Nat Rev Gastroenterol Hepatol 2019; 16:429-447. [PMID: 30988520 DOI: 10.1038/s41575-019-0140-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.
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Affiliation(s)
- Gerard Cummins
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
| | | | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Marc P Y Desmulliez
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Sandy Cochran
- School of Engineering, University of Glasgow, Glasgow, UK
| | - Robert Steele
- School of Medicine, University of Dundee, Dundee, UK
| | - John N Plevris
- Centre for Liver and Digestive Disorders, The Royal Infirmary of Edinburgh, Edinburgh, UK
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Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, Rex DK, Wallace MB. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc 2019; 90:55-63. [PMID: 30926431 DOI: 10.1016/j.gie.2019.03.019] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/18/2019] [Indexed: 02/05/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.
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Affiliation(s)
- Daniela Guerrero Vinsard
- Showa University International Center for Endoscopy, Showa University Northern Yokohama Hospital, Yokohama, Japan; Division of Internal Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Amit Rastogi
- Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas
| | - Ulas Bagci
- Center for Research in Computer Vision, University of Central Florida, Orlando, Florida
| | - Douglas K Rex
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA
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Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc 2019; 89:408-415. [PMID: 30268542 DOI: 10.1016/j.gie.2018.09.024] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 09/20/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS In the treatment of ulcerative colitis (UC), an incremental benefit of achieving histologic healing beyond that of endoscopic mucosal healing has been suggested; persistent histologic inflammation increases the risk of exacerbation and dysplasia. However, identification of persistent histologic inflammation is extremely difficult using conventional endoscopy. Furthermore, the reproducibility of endoscopic disease activity is poor. We developed and evaluated a computer-aided diagnosis (CAD) system to predict persistent histologic inflammation using endocytoscopy (EC; 520-fold ultra-magnifying endoscope). METHODS We evaluated the accuracy of the CAD system using test image sets. First, we retrospectively reviewed the data of 187 patients with UC from whom biopsy samples were obtained after endocytoscopic observation. EC images and biopsy samples of each patient were collected from 6 colorectal segments: cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. All EC images were tagged with reference to the biopsy sample's histologic activity. For validation samples, 525 validation sets of 525 independent segments were collected from 100 patients, and 12,900 EC images from the remaining 87 patients were used for machine learning to construct CAD. The primary outcome measure was the diagnostic ability of CAD to predict persistent histologic inflammation. Its reproducibility for all test images was also assessed. RESULTS CAD provided diagnostic sensitivity, specificity, and accuracy as follows: 74% (95% confidence interval, 65%-81%), 97% (95% confidence interval, 95%-99%), and 91% (95% confidence interval, 83%-95%), respectively. Its reproducibility was perfect (κ = 1). CONCLUSIONS Our CAD system potentially allows fully automated identification of persistent histologic inflammation associated with UC.
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