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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Pal P, Pooja K, Nabi Z, Gupta R, Tandan M, Rao GV, Reddy N. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 2024; 43:172-187. [PMID: 38418774 DOI: 10.1007/s12664-024-01531-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND OBJECTIVES In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas. METHODS We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist. RESULTS The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning. CONCLUSION AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.
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Affiliation(s)
- Partha Pal
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
| | - Kanapuram Pooja
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Zaheer Nabi
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Rajesh Gupta
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Manu Tandan
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Guduru Venkat Rao
- Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India
| | - Nageshwar Reddy
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
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Lv B, Ma L, Shi Y, Tao T, Shi Y. A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis. iScience 2023; 26:108120. [PMID: 37867944 PMCID: PMC10585391 DOI: 10.1016/j.isci.2023.108120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Endoscopic remission is an important therapeutic goal in ulcerative colitis (UC). The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo Endoscopic Score (MES) are the commonly used endoscopic scoring criteria. This systematic review and meta-analysis aimed to evaluate the accuracy of artificial intelligence (AI) in diagnosing endoscopic remission in UC. We also performed a meta-analysis of each of the four endoscopic remission criteria (UCEIS = 0, MES = 0, UCEIS = <1, MES = <1). Eighteen studies involving 13,687 patients were included. The combined sensitivity and specificity of AI for diagnosing endoscopic remission in UC was 87% (95% confidence interval [CI]:81-92%) and 92% (95% CI: 89-94%), respectively. The area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). The results showed that the AI model performed well regardless of which criteria were used to define endoscopic remission of UC.
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Affiliation(s)
- Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, NO.266, Xincunxi Road, Zibo, Shandong 255000, China
| | - Lihong Ma
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanping Shi
- Department of Pediatrics, Zhoucun Maternal and Child Health Care Hospital, No.72 Mianhuashi Street, Zibo, Shandong 255000, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
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Wang G, Zhang S, Li J, Zhao K, Ding Q, Tian D, Li R, Zou F, Yu Q. CB-HRNet: A Class-Balanced High-Resolution Network for the evaluation of endoscopic activity in patients with ulcerative colitis. Clin Transl Sci 2023; 16:1421-1430. [PMID: 37154517 PMCID: PMC10432877 DOI: 10.1111/cts.13542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/11/2023] [Accepted: 04/21/2023] [Indexed: 05/10/2023] Open
Abstract
Endoscopic evaluation is the key to the management of ulcerative colitis (UC). However, there is interobserver variability in interpreting endoscopic images among gastroenterologists. Furthermore, it is time-consuming. Convolutional neural networks (CNNs) can help overcome these obstacles and has yielded preliminary positive results. We aimed to develop a new CNN-based algorithm to improve the performance for evaluation tasks of endoscopic images in patients with UC. A total of 12,163 endoscopic images from 308 patients with UC were collected from January 2014 to December 2021. The training set and test set images were randomly divided into 37,515 and 3191 after excluding possible interference and data augmentation. Mayo Endoscopic Subscores (MES) were predicted by different CNN-based models with different loss functions. Their performances were evaluated by several metrics. After comparing the results of different CNN-based models with different loss functions, High-Resolution Network with Class-Balanced Loss achieved the best performances in all MES classification subtasks. It was especially great at determining endoscopic remission in UC, which achieved a high accuracy of 95.07% and good performances in other evaluation metrics with sensitivity 92.87%, specificity 95.41%, kappa coefficient 0.8836, positive predictive value 93.44%, negative predictive value 95.00% and area value under the receiver operating characteristic curve 0.9834, respectively. In conclusion, we proposed a new CNN-based algorithm, Class-Balanced High-Resolution Network (CB-HRNet), to evaluate endoscopic activity of UC with excellent performance. Besides, we made an open-source dataset and it can be a new benchmark in the task of MES classification.
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Affiliation(s)
- Ge Wang
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Shujiao Zhang
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Jie Li
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kai Zhao
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Qiang Ding
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Dean Tian
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Ruixuan Li
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Fuhao Zou
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Qin Yu
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
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Biamonte P, D’Amico F, Fasulo E, Barà R, Bernardi F, Allocca M, Zilli A, Danese S, Furfaro F. New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients. Biomedicines 2023; 11:2139. [PMID: 37626636 PMCID: PMC10452412 DOI: 10.3390/biomedicines11082139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.
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Affiliation(s)
- Paolo Biamonte
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Rukaia Barà
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Francesca Bernardi
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Gastroenterology and Endoscopy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Federica Furfaro
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
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Kim JE, Choi YH, Lee YC, Seong G, Song JH, Kim TJ, Kim ER, Hong SN, Chang DK, Kim YH, Shin SY. Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis. Sci Rep 2023; 13:11351. [PMID: 37443370 PMCID: PMC10344868 DOI: 10.1038/s41598-023-38206-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC.
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Affiliation(s)
- Ji Eun Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Yoon Ho Choi
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Jacksonville, FL, USA
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Yeong Chan Lee
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
| | - Gyeol Seong
- Department of Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, South Korea
| | - Joo Hye Song
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Tae Jun Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Eun Ran Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Sung Noh Hong
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Dong Kyung Chang
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea
| | - Young-Ho Kim
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea.
| | - Soo-Yong Shin
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, 81 Irwon-Ro, Gangnam-gu, Seoul, 06351, South Korea.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Abstract
Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.
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Affiliation(s)
- Sharib Ali
- grid.9909.90000 0004 1936 8403School of Computing, University of Leeds, LS2 9JT Leeds, UK
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Hsiao SW, Yen HH, Chen YY. Chemoprevention of Colitis-Associated Dysplasia or Cancer in Inflammatory Bowel Disease. Gut Liver 2022; 16:840-848. [PMID: 35670121 PMCID: PMC9668496 DOI: 10.5009/gnl210479] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/20/2021] [Accepted: 12/07/2021] [Indexed: 08/27/2023] Open
Abstract
The association between inflammatory bowel disease and colorectal cancer is well known. Although the overall incidence of inflammatory bowel disease has declined recently, patients with this disease still have a 1.7-fold increased risk of colorectal cancer. The risk factors for developing colorectal cancer include extensive colitis, young age at diagnosis, disease duration, primary sclerosing cholangitis, chronic colonic mucosal inflammation, dysplasia lesion, and post-inflammatory polyps. In patients with inflammatory bowel disease, control of chronic inflammation and surveillance colonoscopies are important for the prevention of colorectal cancer. The 2017 guidelines from the European Crohn's and Colitis Organisation suggest that colonoscopies to screen for colorectal cancer should be performed when inflammatory bowel disease symptoms have lasted for 8 years. Current evidence supports the use of chemoprevention therapy with mesalamine to reduce the risk of colorectal cancer in patients with ulcerative colitis. Other compounds, including thiopurine, folic acid, statin, and tumor necrosis factor-α inhibitor, are controversial. Large surveillance cohort studies with longer follow-up duration are needed to evaluate the impact of drugs on colorectal cancer risks.
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Affiliation(s)
- Shun-Wen Hsiao
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
- Division of Gastroenterology, Yuanlin Christian Hospital, Changhua, Taiwan
| | - Hsu-Heng Yen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
- General Education Center, Chienkuo Technology University, Changhua, Taiwan
- Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yang-Yuan Chen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
- Division of Gastroenterology, Yuanlin Christian Hospital, Changhua, Taiwan
- Department of Hospitality Management, MingDao University, Changhua, Taiwan
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Kawamoto A, Takenaka K, Okamoto R, Watanabe M, Ohtsuka K. Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease. Dig Endosc 2022; 34:1311-1319. [PMID: 35441381 DOI: 10.1111/den.14334] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/18/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data. METHODS We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded. RESULTS A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data. CONCLUSION We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.
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Affiliation(s)
- Ami Kawamoto
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryuichi Okamoto
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mamoru Watanabe
- TMDU Advanced Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kazuo Ohtsuka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan.,Endoscopic Unit, Tokyo Medical and Dental University, Tokyo, Japan
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Yang LS, Perry E, Shan L, Wilding H, Connell W, Thompson AJ, Taylor ACF, Desmond PV, Holt BA. Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review. Endosc Int Open 2022; 10:E1004-E1013. [PMID: 35845028 PMCID: PMC9286774 DOI: 10.1055/a-1846-0642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/02/2022] [Indexed: 12/15/2022] Open
Abstract
Background and aims Artificial intelligence (AI) technology is being evaluated for its potential to improve colonoscopic assessment of inflammatory bowel disease (IBD), particularly with computer-aided image classifiers. This review evaluates the clinical application and diagnostic test accuracy (DTA) of AI algorithms in colonoscopy for IBD. Methods A systematic review was performed on studies evaluating AI in colonoscopy of adult patients with IBD. MEDLINE, Embase, Emcare, PsycINFO, CINAHL, Cochrane Library and Clinicaltrials.gov databases were searched on 28 th April 2021 for English language articles published between January 1, 2000 and April 28, 2021. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic accuracy was presented as median (interquartile range). Results Of 1029 records screened, nine studies with 7813 patients were included for review. AI was used to predict endoscopic and histologic disease activity in ulcerative colitis, and differentiation of Crohn's disease from Behcet's disease and intestinal tuberculosis. DTA of AI algorithms ranged between 52-91 %. The sensitivity and specificity for AI algorithms predicting endoscopic severity of disease were 78 % (range 72-83, interquartile range 5.5) and 91 % (range 86-96, interquartile range 5), respectively. Conclusions AI has been primarily used to assess disease activity in ulcerative colitis. The diagnostic performance is promising and suggests potential for other clinical application of AI in IBD colonoscopy such as dysplasia detection. However, current evidence is limited by retrospective data and models trained on still images only. Future prospective multicenter studies with full-motion videos are needed to replicate the real-world clinical setting.
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Affiliation(s)
- Linda S. Yang
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Evelyn Perry
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Leonard Shan
- Department of Surgery, Faculty of Medicine, Dentistry and Health Sciences, the University of Melbourne, Fitzroy, Victoria, Australia
| | - Helen Wilding
- Library Service, St. Vincent’s Hospital Melbourne, Fitzroy, Victoria, Australia
| | - William Connell
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Alexander J. Thompson
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Andrew C. F. Taylor
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Paul V. Desmond
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
| | - Bronte A. Holt
- Department of Gastroenterology, St. Vincent’s Hospital and the University of Melbourne, Fitzroy, Victoria, Australia
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13
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Higuchi N, Hiraga H, Sasaki Y, Hiraga N, Igarashi S, Hasui K, Ogasawara K, Maeda T, Murai Y, Tatsuta T, Kikuchi H, Chinda D, Mikami T, Matsuzaka M, Sakuraba H, Fukuda S. Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50. PLoS One 2022; 17:e0269728. [PMID: 35687553 PMCID: PMC9187078 DOI: 10.1371/journal.pone.0269728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/26/2022] [Indexed: 12/19/2022] Open
Abstract
Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.
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Affiliation(s)
- Naoki Higuchi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Hiroto Hiraga
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
- * E-mail:
| | - Yoshihiro Sasaki
- Department of Medical Informatics, Hirosaki University Hospital, Hirosaki, Japan
| | - Noriko Hiraga
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shohei Igarashi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Keisuke Hasui
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Kohei Ogasawara
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Takato Maeda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Yasuhisa Murai
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Tetsuya Tatsuta
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Hidezumi Kikuchi
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Daisuke Chinda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Tatsuya Mikami
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Masashi Matsuzaka
- Department of Medical Informatics, Hirosaki University Hospital, Hirosaki, Japan
| | - Hirotake Sakuraba
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shinsaku Fukuda
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
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14
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Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022; 28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
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Affiliation(s)
| | | | - Enrico Mossotto
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Sarah Ennis
- Address correspondence to: Sarah Ennis, Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK ()
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15
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Gargani S, Lourou N, Arapatzi C, Tzanos D, Saridaki M, Dushku E, Chatzimike M, Sidiropoulos ND, Andreadou M, Ntafis V, Hatzis P, Kostourou V, Kontoyiannis DL. Inactivation of AUF1 in Myeloid Cells Protects From Allergic Airway and Tumor Infiltration and Impairs the Adenosine-Induced Polarization of Pro-Angiogenic Macrophages. Front Immunol 2022; 13:752215. [PMID: 35222366 PMCID: PMC8873154 DOI: 10.3389/fimmu.2022.752215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/21/2022] [Indexed: 11/13/2022] Open
Abstract
The four isoforms of the RNA-binding protein hnRNPD/AUF1 have been proposed to limit the use of inflammatory mRNAs in innate immune cells. Mice engineered to lack AUF1s in all tissues are sensitive to acute inflammatory assaults; however, they also manifest complex degenerations obscuring assessment of AUF1s’ roles in innate immune cells. Here, we restricted a debilitating AUF1 mutation to the mouse myeloid lineage and performed disease-oriented phenotypic analyses to assess the requirement of AUF1s in variable contexts of innate immune reactivity. Contrary to the whole-body mutants, the myeloid mutants of AUF1s did not show differences in their susceptibility to cytokine storms occurring during endotoxemia; neither in type-I cell-mediated reactions driving intestinal inflammation by chemical irritants. Instead, they were resistant to allergic airway inflammation and displayed reductions in inflammatory infiltrates and an altered T-helper balance. The ex-vivo analysis of macrophages revealed that the loss of AUF1s had a minimal effect on their proinflammatory gene expression. Moreover, AUF1s were dispensable for the classical polarization of cultured macrophages by LPS & IFNγ correlating with the unchanged response of mutant mice to systemic and intestinal inflammation. Notably, AUF1s were also dispensable for the alternative polarization of macrophages by IL4, TGFβ and IL10, known to be engaged in allergic reactions. In contrast, they were required to switch proinflammatory macrophages towards a pro-angiogenic phenotype induced by adenosine receptor signals. Congruent to this, the myeloid mutants of AUF1 displayed lower levels of vascular remodeling factors in exudates from allergen exposed lungs; were unable to support the growth and inflammatory infiltration of transplanted melanoma tumors; and failed to vascularize inert grafts unless supplemented with angiogenic factors. Mechanistically, adenosine receptor signals enhanced the association of AUF1s with the Vegfa, Il12b, and Tnf mRNAs to differentially regulate and facilitate the pro-angiogenic switch. Our data collectively demonstrates that AUF1s do not act as general anti-inflammatory factors in innate immune cells but have more specialized roles in regulons allowing specific innate immune cell transitions to support tissue infiltration and remodeling processes.
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Affiliation(s)
- Sofia Gargani
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Niki Lourou
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christina Arapatzi
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Dimitris Tzanos
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Marania Saridaki
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Esmeralda Dushku
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Margarita Chatzimike
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Nikolaos D. Sidiropoulos
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Margarita Andreadou
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Vasileios Ntafis
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Pantelis Hatzis
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Vassiliki Kostourou
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
| | - Dimitris L. Kontoyiannis
- Biomedical Sciences Research Centre “Alexander Fleming”, Institute of Fundamental Biomedical Research, Vari, Greece
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- *Correspondence: Dimitris L. Kontoyiannis, ;
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16
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Solitano V, Zilli A, Franchellucci G, Allocca M, Fiorino G, Furfaro F, D’amico F, Danese S, Al Awadhi S. Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future. J Clin Med 2022; 11:569. [PMID: 35160021 PMCID: PMC8836846 DOI: 10.3390/jcm11030569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.
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17
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Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021; 39:2207-2222. [PMID: 34744231 PMCID: PMC8557108 DOI: 10.1007/s00371-021-02322-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 05/07/2023]
Abstract
With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Jixiang Zhang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
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18
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Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artif Intell Gastrointest Endosc 2021; 2:95-102. [DOI: 10.37126/aige.v2.i4.95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/27/2021] [Accepted: 08/16/2021] [Indexed: 02/06/2023] Open
Abstract
Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.
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Affiliation(s)
- Bobby Lo
- Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
- Copenhagen Centre for Inflammatory Bowel Disease in Children, Adolescents and Adults, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
| | - Johan Burisch
- Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
- Copenhagen Centre for Inflammatory Bowel Disease in Children, Adolescents and Adults, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
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Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27:4395-4412. [PMID: 34366612 PMCID: PMC8316909 DOI: 10.3748/wjg.v27.i27.4395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/14/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.
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Affiliation(s)
| | - José Aneiros-Fernández
- Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain
| | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, Malaga 29016, Spain
| | - Antonio Luna
- MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain
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