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Iacucci M, Santacroce G, Zammarchi I, Maeda Y, Del Amor R, Meseguer P, Kolawole BB, Chaudhari U, Di Sabatino A, Danese S, Mori Y, Grisan E, Naranjo V, Ghosh S. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2024; 9:758-772. [PMID: 38759661 DOI: 10.1016/s2468-1253(24)00053-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 05/19/2024]
Abstract
Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain; Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | | | | | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy; First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
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Subudhi RN, Poonia N, Singh D, Arora V. Natural approaches for the management of ulcerative colitis: evidence of preclinical and clinical investigations. NATURAL PRODUCTS AND BIOPROSPECTING 2024; 14:42. [PMID: 39078427 PMCID: PMC11289194 DOI: 10.1007/s13659-024-00463-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 07/07/2024] [Indexed: 07/31/2024]
Abstract
Ulcerative colitis (UC) is a recurring autoimmune disorder characterized by persistent inflammation in the mucosal lining of the lower part of the large intestine. Conventional treatment options such as salicylates, corticosteroids, and immunosuppressants often come with severe side effects, limited bioavailability, and the development of drug resistance, which hampers their therapeutic effectiveness. Therefore, it is imperative to explore natural strategies as safe and alternative treatments for UC. Currently, around 40% of UC patients find relief through natural constituents, which can help reduce toxic side effects and maintain clinical remission. This review aims to provide a summary of both preclinical and clinical evidence supporting the efficacy of various natural substances in the prophylaxis of UC. These natural options include plant extracts, essential oils, nutraceuticals, and phytochemicals. Furthermore, we will delve into the potential mechanisms that underlie the protective and curative actions of these novel herbal agents. In summary, this review will explore the effectiveness of natural remedies for UC, shedding light on their preclinical and clinical findings and the mechanisms behind their therapeutic actions. These alternatives offer hope for improved treatment outcomes and reduced side effects for individuals suffering from this challenging autoimmune condition.
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Affiliation(s)
- Rudra Narayan Subudhi
- Department of Pharmaceutics, University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, Punjab, India
| | - Neelam Poonia
- Department of Pharmaceutics, University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, Punjab, India.
| | - Dilpreet Singh
- Department of Pharmaceutics, University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, Punjab, India
| | - Vimal Arora
- Department of Pharmaceutics, University Institute of Pharma Sciences, Chandigarh University, Gharuan, Mohali, Punjab, India
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Silverman AL, Shung D, Stidham RW, Kochhar GS, Iacucci M. How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00598-6. [PMID: 38992406 DOI: 10.1016/j.cgh.2024.05.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.
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Affiliation(s)
- Anna L Silverman
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Scottsdale, Arizona.
| | - Dennis Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Ryan W Stidham
- Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan
| | - Gursimran S Kochhar
- Division of Gastroenterology, Hepatology, and Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Marietta Iacucci
- University of Birmingham, Institute of Immunology and Immunotherapy, Birmingham, United Kingdom; College of Medicine and Health, University College Cork, and APC Microbiome Ireland, Cork, Ireland
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Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis 2024; 56:1164-1172. [PMID: 38057218 DOI: 10.1016/j.dld.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUNDS AND AIMS Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.
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Affiliation(s)
- Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy
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Maeda Y, Kudo SE, Santacroce G, Ogata N, Misawa M, Iacucci M. Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review. Dig Liver Dis 2024; 56:1119-1125. [PMID: 38643020 DOI: 10.1016/j.dld.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/22/2024]
Abstract
This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.
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Affiliation(s)
- Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan; APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland.
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
| | - Noriyuki Ogata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama 224-8503, Japan
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, T12 YT20, Ireland
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Furlanello C, Bussola N, Merzi N, Pievani Trapletti G, Cadei M, Del Sordo R, Sidoni A, Ricci C, Lanzarotto F, Parigi TL, Villanacci V. The development of artificial intelligence in the histological diagnosis of Inflammatory Bowel Disease (IBD-AI). Dig Liver Dis 2024:S1590-8658(24)00791-6. [PMID: 38853093 DOI: 10.1016/j.dld.2024.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 05/12/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretation can be challenging, and it is subject to high variability. AIM The IBD-Artificial Intelligence (AI) project aims at the development of an AI-based evaluation system to support the diagnosis of IBD, semi-automatically quantifying basal plasmacytosis. METHODS A deep learning model was trained to detect and quantify plasma cells on a public dataset of 4981 annotated images. The model was then tested on an external validation cohort of 356 intestinal biopsies of CD, UC and healthy controls. AI diagnostic performance was calculated compared to human gold standard. RESULTS The system correctly found that CD and UC samples had a greater prevalence of basal plasma cells with mean number of PCs within ROIs of 38.22 (95 % CI: 31.73, 49.04) for CD, 55.16 (46.57, 65.93) for UC, and 17.25 (CI: 12.17, 27.05) for controls. Overall, OR=4.968 (CI: 1.835, 14.638) was found for IBD compared to normal mucosa (CD: +59 %; UC: +129 %). Additionally, as expected, UC samples were found to have more plasma cells in colon than CD cases. CONCLUSION Our model accurately replicated human assessment of basal plasmacytosis, underscoring the value of AI models as a potential aid IBD diagnosis.
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Affiliation(s)
| | | | | | | | - Moris Cadei
- Institute of Pathology, ASST Spedali Civili and University of Brescia, Brescia, Italy
| | - Rachele Del Sordo
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Angelo Sidoni
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Chiara Ricci
- Gastroenterology Unit, Clinical and Experimental Sciences Department, Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Francesco Lanzarotto
- Gastroenterology Unit, Clinical and Experimental Sciences Department, Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Tommaso Lorenzo Parigi
- Division of Immunology, Transplantation and Infectious Disease, University Vita-Salute San Raffaele, Milan, Italy
| | - Vincenzo Villanacci
- Institute of Pathology, ASST Spedali Civili and University of Brescia, Brescia, Italy.
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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] [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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Santacroce G, Zammarchi I, Tan CK, Coppola G, Varley R, Ghosh S, Iacucci M. Present and future of endoscopy precision for inflammatory bowel disease. Dig Endosc 2024; 36:292-304. [PMID: 37643635 DOI: 10.1111/den.14672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 08/31/2023]
Abstract
Several advanced imaging techniques are now available for endoscopists managing inflammatory bowel disease (IBD) patients. These tools, including dye-based and virtual chromoendoscopy, probe-based confocal laser endomicroscopy and endocytoscopy, are increasingly innovative applications in clinical practice. They allow for a more in-depth and refined evaluation of the mucosal and vascular bowel surface, getting closer to histology. They have demonstrated a remarkable ability in assessing intestinal inflammation, histologic remission, and predicting relapse and favorable long-term outcomes. In addition, the future application of molecular endoscopy to predict biological drug responses has yielded preliminary but encouraging results. Furthermore, these techniques are crucial in detecting and characterizing IBD-related dysplasia, assisting endoscopic mucosal resection and submucosal dissection towards a surgery-sparing approach. Artificial intelligence (AI) holds great potential in this promising landscape, as it can provide an objective and reproducible assessment of inflammation and dysplasia. Moreover, it can improve the prediction of outcomes and aid in subsequent therapeutic decision-making. This review aims to summarize the promising role of state-of-the-art advanced endoscopic techniques and related AI-enabled models for managing IBD, paving the way for precision medicine.
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Affiliation(s)
- Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Chin Kimg Tan
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
- Gastroenterology and Hepatology, Changi General Hospital, Singapore City, Singapore
| | - Gaetano Coppola
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
- Internal Medicine and Gastroenterology - Hepatology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Rachel Varley
- Department of Gastroenterology, Mercy University Hospital, Cork, Ireland
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland
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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] [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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Omori T, Yamamoto T, Murasugi S, Koroku M, Yonezawa M, Nonaka K, Nagashima Y, Nakamura S, Tokushige K. Comparison of Endoscopic and Artificial Intelligence Diagnoses for Predicting the Histological Healing of Ulcerative Colitis in a Real-World Clinical Setting. CROHN'S & COLITIS 360 2024; 6:otae005. [PMID: 38419859 PMCID: PMC10901431 DOI: 10.1093/crocol/otae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Indexed: 03/02/2024] Open
Abstract
Background Artificial intelligence (AI)-assisted colonoscopy systems with contact microscopy capabilities have been reported previously; however, no studies regarding the clinical use of a commercially available system in patients with ulcerative colitis (UC) have been reported. In this study, the diagnostic performance of an AI-assisted ultra-magnifying colonoscopy system for histological healing was compared with that of conventional light non-magnifying endoscopic evaluation in patients with UC. Methods The data of 52 patients with UC were retrospectively analyzed. The Mayo endoscopic score (MES) was determined by 3 endoscopists. Using the AI system, healing of the same spot assessed via MES was defined as a predicted Geboes score (GS) < 3.1. The GS was then determined using pathology specimens from the same site. Results A total of 191 sites were evaluated, including 159 with a GS < 3.1. The MES diagnosis identified 130 sites as MES0. A total of 120 sites were determined to have healed based on AI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MES0 for the diagnosis of GS < 3.1 were 79.2%, 90.6%, 97.7%, 46.8%, and 81.2%, respectively. The AI system performed similarly to MES for the diagnosis of GS < 3.1: sensitivity, 74.2%; specificity: 93.8%; PPV: 98.3%; NPV: 42.3%; and accuracy: 77.5%. The AI system also significantly identified a GS of < 3.1 in the setting of MES1 (P = .0169). Conclusions The histological diagnostic yield the MES- and AI-assisted diagnoses was comparable. Healing decisions using AI may avoid the need for histological examinations.
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Affiliation(s)
- Teppei Omori
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Tomoko Yamamoto
- Department of Surgical Pathology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shun Murasugi
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Miki Koroku
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Maria Yonezawa
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Kouichi Nonaka
- Department of Digestive Endoscopy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Yoji Nagashima
- Department of Surgical Pathology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shinichi Nakamura
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
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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] [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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12
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Jahagirdar V, Bapaye J, Chandan S, Ponnada S, Kochhar GS, Navaneethan U, Mohan BP. Diagnostic accuracy of convolutional neural network-based machine learning algorithms in endoscopic severity prediction of ulcerative colitis: a systematic review and meta-analysis. Gastrointest Endosc 2023; 98:145-154.e8. [PMID: 37094691 DOI: 10.1016/j.gie.2023.04.2074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/06/2023] [Accepted: 04/16/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND AND AIMS Endoscopic assessment of ulcerative colitis (UC) can be performed by using the Mayo Endoscopic Score (MES) or the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). In this meta-analysis, we assessed the pooled diagnostic accuracy parameters of deep machine learning by means of convolutional neural network (CNN) algorithms in predicting UC severity on endoscopic images. METHODS Databases including MEDLINE, Scopus, and Embase were searched in June 2022. Outcomes of interest were the pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Standard meta-analysis methods used the random-effects model, and heterogeneity was assessed using the I2statistics. RESULTS Twelve studies were included in the final analysis. The pooled diagnostic parameters of CNN-based machine learning algorithms in endoscopic severity assessment of UC were as follows: accuracy 91.5% (95% confidence interval [CI], 88.3-93.8; I2 = 84%), sensitivity 82.8% (95% CI, 78.3-86.5; I2 = 89%), specificity 92.4% (95% CI, 89.4-94.6; I2 = 84%), PPV 86.6% (95% CI, 82.3-90; I2 = 89%), and NPV 88.6% (95% CI, 85.7-91; I2 = 78%). Subgroup analysis revealed significantly better sensitivity and PPV with the UCEIS scoring system compared with the MES (93.6% [95% CI, 87.5-96.8; I2 = 77%] vs 82% [95% CI, 75.6-87; I2 = 89%], P = .003, and 93.6% [95% CI, 88.7-96.4; I2 = 68%] vs 83.6% [95% CI, 76.8-88.8; I2 = 77%], P = .007, respectively). CONCLUSIONS CNN-based machine learning algorithms demonstrated excellent pooled diagnostic accuracy parameters in the endoscopic severity assessment of UC. Using UCEIS scores in CNN training might offer better results than the MES. Further studies are warranted to establish these findings in real clinical settings.
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Affiliation(s)
- Vinay Jahagirdar
- Department of Internal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, New York, USA
| | - Saurabh Chandan
- Department of Gastroenterology, Creighton University Medical Center, Creighton, Nebraska, USA
| | - Suresh Ponnada
- Internal Medicine, Roanoke Carilion Hospital, Roanoke, Virginia, USA
| | - Gursimran S Kochhar
- Department of Gastroenterology & Hepatology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA
| | | | - Babu P Mohan
- Department of Gastroenterology & Hepatology, University of Utah, Salt Lake City, Utah, USA
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13
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Zammarchi I, Santacroce G, Iacucci M. Next-Generation Endoscopy in Inflammatory Bowel Disease. Diagnostics (Basel) 2023; 13:2547. [PMID: 37568910 PMCID: PMC10417286 DOI: 10.3390/diagnostics13152547] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023] Open
Abstract
Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.
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Affiliation(s)
| | | | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, T12 R229 Cork, Ireland; (I.Z.); (G.S.)
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14
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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] [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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15
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Zulqarnain F, Rhoads SF, Syed S. Machine and deep learning in inflammatory bowel disease. Curr Opin Gastroenterol 2023; 39:294-300. [PMID: 37144491 PMCID: PMC10256313 DOI: 10.1097/mog.0000000000000945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE OF REVIEW The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients. RECENT FINDINGS Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment. SUMMARY Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.
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Affiliation(s)
- Fatima Zulqarnain
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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16
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Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29:508-520. [PMID: 36688019 PMCID: PMC9850939 DOI: 10.3748/wjg.v29.i3.508] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/05/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Inflammatory bowel diseases, namely ulcerative colitis and Crohn’s disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.
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Affiliation(s)
- Leonardo Da Rio
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberto Gabbiadini
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Arianna Dal Buono
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Anita Busacca
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Ludovico Alfarone
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia 71122, Foggia, Italy
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Armuzzi
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
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17
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Danese S. Artificial intelligence in endoscopic assessment of ulcerative colitis: virtual painting with PICaSSO. Endoscopy 2023; 55:342-343. [PMID: 36630973 DOI: 10.1055/a-1986-7532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Silvio Danese
- Gastroenterology and Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy.,University Vita-Salute San Raffaele, Milan, Italy
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18
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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] [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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19
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Iacucci M, Cannatelli R, Parigi TL, Nardone OM, Tontini GE, Labarile N, Buda A, Rimondi A, Bazarova A, Bisschops R, Del Amor R, Meseguer P, Naranjo V, Ghosh S, Grisan E. A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis. Endoscopy 2022; 55:332-341. [PMID: 36228649 PMCID: PMC10060056 DOI: 10.1055/a-1960-3645] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. METHODS 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. RESULTS The AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores. CONCLUSIONS Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.
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Affiliation(s)
- Marietta Iacucci
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.,Division of Gastroenterology and Hepatology, University of Calgary, Calgary, Canada
| | - Rosanna Cannatelli
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,Gastroenterology and Digestive Endoscopy Unit, Department of Biochemical and Clinical Sciences "L. Sacco", University of Milan, ASST Fatebenefratelli Sacco, Milan, Italy
| | - Tommaso L Parigi
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Olga M Nardone
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,Gastroenterology, department of Public health, university of Naples Federico II, Naples, Italy
| | - Gian Eugenio Tontini
- Division of Gastroenterology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Nunzia Labarile
- National Institute of Gastroenterology, IRCCS S. De Bellis Research Hospital, Castellana Grotte, Italy
| | - Andrea Buda
- Department of Gastrointestinal Oncological Surgery, Santa Maria del Prato Hospital, Feltre, Italy
| | - Alessandro Rimondi
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alina Bazarova
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,Institute for Biological Physics, University of Cologne, Cologne, Germany
| | - Raf Bisschops
- Division of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
| | - Rocio Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- Institute of Immunology and Immunotherapy, NIHR Wellcome Trust Clinical Research Facilities, University of Birmingham, and University Hospitals Birmingham NHS Trust, Birmingham, UK.,National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.,Division of Gastroenterology and Hepatology, University of Calgary, Calgary, Canada.,APC Microbiome Ireland, College of Medicine and Health, Cork, Ireland
| | - Enrico Grisan
- School of Engineering Computer Science and Informatics, London South Bank University, London, UK.,Department of Engineering, University of Padova, Padova, Italy
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20
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Alfarone L, Parigi TL, Gabbiadini R, Dal Buono A, Spinelli A, Hassan C, Iacucci M, Repici A, Armuzzi A. Technological advances in inflammatory bowel disease endoscopy and histology. Front Med (Lausanne) 2022; 9:1058875. [PMID: 36438050 PMCID: PMC9691880 DOI: 10.3389/fmed.2022.1058875] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/26/2022] [Indexed: 09/29/2023] Open
Abstract
Accurate disease characterization is the pillar of modern treatment of inflammatory bowel disease (IBD) and endoscopy is the mainstay of disease assessment and colorectal cancer surveillance. Recent technological progress has enhanced and expanded the use of endoscopy in IBD. In particular, numerous artificial intelligence (AI)-powered systems have shown to support human endoscopists' evaluations, improving accuracy and consistency while saving time. Moreover, advanced optical technologies such as endocytoscopy (EC), allowing high magnification in vivo, can bridge endoscopy with histology. Furthermore, molecular imaging, through probe based confocal laser endomicroscopy allows the real-time detection of specific biomarkers on gastrointestinal surface, and could be used to predict therapeutic response, paving the way to precision medicine. In parallel, as the applications of AI spread, computers are positioned to resolve some of the limitations of human histopathology evaluation, such as interobserver variability and inconsistencies in assessment. The aim of this review is to summarize the most promising advances in endoscopic and histologic assessment of IBD.
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Affiliation(s)
- Ludovico Alfarone
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
| | | | | | - Antonino Spinelli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Colon and Rectal Surgery Division, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Marietta Iacucci
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
- Department of Gastroenterology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Endoscopy Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Alessandro Armuzzi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IBD Center, IRCCS Humanitas Research Hospital, Milan, Italy
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21
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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] [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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22
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Computer copilots for endoscopic diagnosis. NPJ Digit Med 2022; 5:129. [PMID: 36050460 PMCID: PMC9436955 DOI: 10.1038/s41746-022-00678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/15/2022] [Indexed: 11/08/2022] Open
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23
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Colitis ulcerosa: Künstliche Intelligenz sagt histologische Remission voraus. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2022. [DOI: 10.1055/a-1795-5187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Gui X, Bazarova A, del Amor R, Vieth M, de Hertogh G, Villanacci V, Zardo D, Parigi TL, Røyset ES, Shivaji UN, Monica MAT, Mandelli G, Bhandari P, Danese S, Ferraz JG, Hayee B, Lazarev M, Parra-Blanco A, Pastorelli L, Panaccione R, Rath T, Tontini GE, Kiesslich R, Bisschops R, Grisan E, Naranjo V, Ghosh S, Iacucci M. PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system. Gut 2022; 71:889-898. [PMID: 35173041 PMCID: PMC8995819 DOI: 10.1136/gutjnl-2021-326376] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/20/2022] [Indexed: 12/17/2022]
Abstract
UNLABELLED Histological remission is evolving as an important treatment target in UC. We aimed to develop a simple histological index, aligned to endoscopy, correlated with clinical outcomes, and suited to apply to an artificial intelligence (AI) system to evaluate inflammatory activity. METHODS Using a set of 614 biopsies from 307 patients with UC enrolled into a prospective multicentre study, we developed the Paddington International virtual ChromoendoScopy ScOre (PICaSSO) Histologic Remission Index (PHRI). Agreement with multiple other histological indices and validation for inter-reader reproducibility were assessed. Finally, to implement PHRI into a computer-aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies. RESULTS PHRI is strongly correlated with endoscopic scores (Mayo Endoscopic Score and UC Endoscopic Index of Severity and PICaSSO) and with clinical outcomes (hospitalisation, colectomy and initiation or changes in medical therapy due to UC flare-up). A PHRI score of 1 could accurately stratify patients' risk of adverse outcomes (hospitalisation, colectomy and treatment optimisation due to flare-up) within 12 months. Our inter-reader agreement was high (intraclass correlation 0.84). Our preliminary AI algorithm differentiated active from quiescent UC with 78% sensitivity, 91.7% specificity and 86% accuracy. CONCLUSIONS PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRI-based AI system was accurate in predicting histological remission.
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Affiliation(s)
- Xianyong Gui
- Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Alina Bazarova
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK,Institute for Biological Physics, University of Cologne, Koln, Germany
| | - Rocìo del Amor
- Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politecnica de Valencia, Valencia, Spain
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbH, Bayreuth, Germany,Institute of Pathology, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany
| | - Gert de Hertogh
- Department of Pathology, KU Leuven University Hospitals Leuven, Leuven, Belgium
| | | | - Davide Zardo
- Department of Cellular Pathology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy,Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Elin Synnøve Røyset
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Uday N Shivaji
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK,Gastroenterology, National Institute of Health Research Birmingham Biomedical Research Unit, Birmingham, UK
| | | | - Giulio Mandelli
- Department of Pathology, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Pradeep Bhandari
- Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK
| | - Silvio Danese
- Department of Gastroenterology and Endoscopy, Università Vita Salute San Raffaele, Milano, Italy,Department of Gastroenterology and Endoscopy, San Raffaele Hospital, Milano, Italy
| | - Jose G Ferraz
- Division of Gastroenterology, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Bu'Hussain Hayee
- King's Health Partners Institute for Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Mark Lazarev
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Adolfo Parra-Blanco
- Department of Gastroenterology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Luca Pastorelli
- Gastroenterology Unit, IRCCS Policlinico San Donato, San Donato Milanese, Italy,Department of Health Sciences, University of Milan, Milan, Italy
| | - Remo Panaccione
- Division of Gastroenterology, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Timo Rath
- Department of Gastoenterology, University of Erlangen Nuremberg—Nuremberg Campus, Nurnberg, Germany
| | - Gian Eugenio Tontini
- Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Ralf Kiesslich
- Department of Gastroenterology, Helios HSK, Wiesbaden, Germany
| | - Raf Bisschops
- Department of Gastroenterology, KU Leuven University Hospitals Leuven, Leuven, Belgium
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK,Department of Information Engineering, Università degli Studi di Padova, Padova, Italy
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politecnica de Valencia, Valencia, Spain
| | - Subrata Ghosh
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK,APC Microbiome, Ireland, University College Cork, Cork, Ireland
| | - Marietta Iacucci
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK .,Gastroenterology, National Institute of Health Research Birmingham Biomedical Research Unit, Birmingham, UK.,Department of Gastroenterology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
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25
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Parigi TL, Mastrorocco E, Da Rio L, Allocca M, D’Amico F, Zilli A, Fiorino G, Danese S, Furfaro F. Evolution and New Horizons of Endoscopy in Inflammatory Bowel Diseases. J Clin Med 2022; 11:jcm11030872. [PMID: 35160322 PMCID: PMC8837111 DOI: 10.3390/jcm11030872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 01/30/2022] [Accepted: 02/01/2022] [Indexed: 12/15/2022] Open
Abstract
Endoscopy is the mainstay of inflammatory bowel disease (IBD) evaluation and the pillar of colorectal cancer surveillance. Endoscopic equipment, both hardware and software, are advancing at an incredible pace. Virtual chromoendoscopy is now widely available, allowing the detection of subtle inflammatory changes, thus reducing the gap between endoscopic and histologic assessment. The progress in the field of artificial intelligence (AI) has been remarkable, and numerous applications are now in an advanced stage of development. Computer-aided diagnosis (CAD) systems are likely to reshape most of the evaluations that are now prerogative of human endoscopists. Furthermore, sophisticated tools such as endocytoscopy and probe-based confocal laser endomicroscopy (pCLE) are enhancing our assessment of inflammation and dysplasia. Finally, pCLE combined with molecular labeling could pave the way to a new paradigm of personalized medicine. This review aims to summarize the main changes that occurred in the field of IBD endoscopy and to explore the most promising novelties.
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Affiliation(s)
- Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham B15 2TT, UK
| | - Elisabetta Mastrorocco
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
| | - Leonardo Da Rio
- Department of Biomedical Sciences, Humanitas University, 20090 Milan, Italy; (T.L.P.); (E.M.); (L.D.R.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Gionata Fiorino
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.A.); (F.D.); (A.Z.); (G.F.); (S.D.)
| | - Federica Furfaro
- IBD Center, Humanitas Research Hospital, 20089 Milan, Italy
- Correspondence: ; Tel.: +39-0282245555
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