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Rymarczyk D, Schultz W, Borowa A, Friedman JR, Danel T, Branigan P, Chałupczak M, Bracha A, Krawiec T, Warchoł M, Li K, De Hertogh G, Zieliński B, Ghanem LR, Stojmirovic A. Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity. J Crohns Colitis 2024; 18:604-614. [PMID: 37814351 PMCID: PMC11037111 DOI: 10.1093/ecco-jcc/jjad171] [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] [Received: 05/11/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND AND AIMS Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD. METHODS Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets, and model predictions were compared against an expert central reader and five independent pathologists. RESULTS The model based on multiple instance learning and the attention mechanism [SA-AbMILP] demonstrated the best performance among competing models. AI-modelled GHAS and Geboes subgrades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features, with accuracies for colon in both CD and UC ranging from 87% to 94% and for CD ileum ranging from 76% to 83%. For both CD and UC and across anatomical compartments [ileum and colon] in CD, comparable accuracies against central readings were found between the model-assigned scores and scores by an independent set of pathologists. CONCLUSIONS Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.
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Affiliation(s)
- Dawid Rymarczyk
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Weiwei Schultz
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Adriana Borowa
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Joshua R Friedman
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Tomasz Danel
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Patrick Branigan
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | | | | | | | | | - Katherine Li
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Gert De Hertogh
- Department of Pathology, University Hospitals KU Leuven, Belgium
| | - Bartosz Zieliński
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Louis R Ghanem
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Aleksandar Stojmirovic
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024:izae030. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [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: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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3
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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4
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Chierici M, Puica N, Pozzi M, Capistrano A, Donzella MD, Colangelo A, Osmani V, Jurman G. Automatically detecting Crohn's disease and Ulcerative Colitis from endoscopic imaging. BMC Med Inform Decis Mak 2022; 22:300. [PMID: 36401328 PMCID: PMC9675066 DOI: 10.1186/s12911-022-02043-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn's disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N). METHODS In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn's Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N). RESULTS The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD. CONCLUSION Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.
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Affiliation(s)
- Marco Chierici
- Fondazione Bruno Kessler, via Sommarive, 18, 38123 Trento, Italy
| | | | - Matteo Pozzi
- Fondazione Bruno Kessler, via Sommarive, 18, 38123 Trento, Italy
- Università degli studi di Trento, via Calepina, 14, 38122 Trento, Italy
| | | | | | | | - Venet Osmani
- Fondazione Bruno Kessler, via Sommarive, 18, 38123 Trento, Italy
| | - Giuseppe Jurman
- Fondazione Bruno Kessler, via Sommarive, 18, 38123 Trento, Italy
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5
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Stidham RW, Takenaka K. Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice? Gastroenterology 2022; 162:1493-1506. [PMID: 34995537 PMCID: PMC8997186 DOI: 10.1053/j.gastro.2021.12.238] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/02/2021] [Accepted: 12/06/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.
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Affiliation(s)
- Ryan W. Stidham
- Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
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Rezazadeh Ardabili A, Goudkade D, Wintjens D, Romberg-Camps M, Winkens B, Pierik M, Grabsch HI, Jonkers D. Histopathological Features in Colonic Biopsies at Diagnosis Predict Long-term Disease Course in Patients with Crohn's Disease. J Crohns Colitis 2021; 15:1885-1897. [PMID: 33987670 PMCID: PMC8575048 DOI: 10.1093/ecco-jcc/jjab087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND AND AIMS Crohn's disease [CD] is characterised by a heterogeneous disease course. Patient stratification at diagnosis using clinical, serological, or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features to discriminate between a long-term mild and severe disease course. METHODS Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis were reviewed by two gastrointestinal pathologists after developing a standardised form comprising 15 histopathological features. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristic [ROC] curves. Models were internally validated using bootstrapping to obtain optimism-corrected performance estimates. RESULTS In total, 817 biopsies from 137 patients [64 mild, 73 severe cases] were included. Using clinical baseline characteristics, disease course could only moderately be predicted (area under receiver operating characteristic curve [AUROC]: 0.738 [optimism 0.018], 95% confidence interval [CI] 0.65-0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic biopsies a combination of [1] basal plasmacytosis, [2] severe lymphocyte infiltration in lamina propria, [3] Paneth cell metaplasia, and [4] absence of ulcers were identified and resulted in significantly better prediction of a severe course (AUROC: 0.883 [optimism 0.033], 95% CI 0.82-0.94, sensitivity 80.4%, specificity 84.2%). CONCLUSIONS In this first study investigating the additive predictive value of histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improving stratification and facilitating clinical decision making.
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Affiliation(s)
- Ashkan Rezazadeh Ardabili
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
- Corresponding author: Ashkan Rezazadeh Ardabili, MD, Department of Internal Medicine, Division of Gastroenterology and Hepatology, NUTRIM, School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Postbox 5800, 6202 AZ, Maastricht, The Netherlands. Tel.: 0031-43-3884203; fax: 0031-43-3875006;
| | - Danny Goudkade
- Department of Pathology, Zuyderland Medical Centre, Geleen, The Netherlands
| | - Dion Wintjens
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Mariëlle Romberg-Camps
- Department of Gastroenterology, Geriatrics, Internal and Intensive Care Medicine, Zuyderland Medical Centre, Sittard-Geleen, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Marie Pierik
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
- Division of Pathology & Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK
| | - Daisy Jonkers
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
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7
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Nguyen NH, Picetti D, Dulai PS, Jairath V, Sandborn WJ, Ohno-Machado L, Chen PL, Singh S. Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review. J Crohns Colitis 2021; 16:398-413. [PMID: 34492100 PMCID: PMC8919806 DOI: 10.1093/ecco-jcc/jjab155] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases [IBD]. We synthesised and critically appraised studies comparing machine learning vs traditional statistical models, using routinely available clinical data for risk prediction in IBD. METHODS Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harbouring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment [PROBAST] tool. RESULTS We included 13 studies on machine learning-based prediction models in IBD, encompassing themes of predicting treatment response to biologics and thiopurines and predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learning models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. CONCLUSIONS Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.
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Affiliation(s)
| | | | - Parambir S Dulai
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Vipul Jairath
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada,Division of Gastroenterology, Western University, London, ON, Canada
| | - William J Sandborn
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lucila Ohno-Machado
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | | | - Siddharth Singh
- Corresponding author: Siddharth Singh, MD, MS, Division of Gastroenterology and Division of Biomedical Informatics, University of California San Diego, 9452 Medical Centre Dr., ACTRI 1W501, La Jolla, CA 92093, USA. Tel.: 858-246-2352; fax: 858-657-7259;
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8
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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] [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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9
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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10
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Syed S, Stidham RW. Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease. Inflamm Bowel Dis 2020; 26:1490-1497. [PMID: 32869844 PMCID: PMC7749192 DOI: 10.1093/ibd/izaa211] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Indexed: 02/07/2023]
Abstract
Automated image analysis methods have shown potential for replicating expert interpretation of histology and endoscopy images, which traditionally require highly specialized and experienced reviewers. Inflammatory bowel disease (IBD) diagnosis, severity assessment, and treatment decision-making require multimodal expert data interpretation and integration, which could be significantly aided by applications of machine learning analyses. This review introduces fundamental concepts of machine learning for imaging analysis and highlights research and development of automated histology and endoscopy interpretation in IBD. Proof-of-concept studies strongly suggest that histologic and endoscopic images can be interpreted with similar accuracy as knowledge experts. Encouraging results support the potential of automating existing disease activity scoring instruments with high reproducibility, speed, and accessibility, therefore improving the standardization of IBD assessment. Though challenges surrounding ground truth definitions, technical barriers, and the need for extensive multicenter evaluation must be resolved before clinical implementation, automated image analysis is likely to both improve access to standardized IBD assessment and advance the fundamental concepts of how disease is measured.
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Affiliation(s)
- Sana Syed
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA,Address correspondence to: Ryan W. Stidham, MD, MS, Assistant Professor of Medicine, Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, University of Michigan Medical School, 3912 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.
| | - Ryan W Stidham
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA,Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), University of Michigan, Ann Arbor, MI, USA
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11
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Siegel CA, Bernstein CN. Identifying Patients With Inflammatory Bowel Diseases at High vs Low Risk of Complications. Clin Gastroenterol Hepatol 2020; 18:1261-1267. [PMID: 31778805 DOI: 10.1016/j.cgh.2019.11.034] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 11/10/2019] [Accepted: 11/19/2019] [Indexed: 02/07/2023]
Abstract
People with Crohn's disease and ulcerative colitis have varying presentations and clinical consequences of their disease. Patients commonly ask about their prognosis, and what this diagnosis means for them. They are asking their clinicians to predict the future. The importance of predicting the course of any disease is to guide patient expectations and to guide treatment decisions. In the past decade the strategy of inflammatory bowel disease (IBD) treatment has shifted to treat patients earlier in the course of their disease, before irreversible damage occurs. Treatment approaches for disease categorized as mild, moderate or severe has most often been based on a current assessment of symptoms or disease activity without including a longitudinal assessment of a patient's disease course including past disease complications and surgeries. While a patient's current disease activity most typically drives these treatment decisions, optimally, treatment decisions would be made accounting for past disease activity and complications and the predicted future disease course. When developing a treatment plan for an individual patient, the immediate goal is to treat the current disease activity for relief of symptoms, and the long-term goal is to prevent progression of their disease due to complications. Since not all patients will progress to a complicated disease course, it is important to be able to select the right patients for the right therapy. Therefore, developing methods of stratifying patients into low-risk versus high-risk of complications will be an important aspect of treating IBD now and in the future.
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Affiliation(s)
- Corey A Siegel
- Dartmouth-Hitchcock Inflammatory Bowel Disease Center, Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
| | - Charles N Bernstein
- Section of Gastroenterology, Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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12
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Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 285] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
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Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
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Crispel Y, Shaoul R, Khamaise R, Sabo E, Hochberg Z. Effect of weaning age on the small intestine mucosa of rats. Appl Physiol Nutr Metab 2019; 44:985-989. [PMID: 30689399 DOI: 10.1139/apnm-2018-0454] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Weaning of mammalian progeny is associated with a change in food composition and mother-offspring separation. Weaning results in a critical period of low voluntary feed intake, during which the animal is adapting to the starter diet. To evaluate the effects of weaning age on morphological changes that occur in the intestines of rats, we assessed intestinal histomorphometry and somatic growth in 21-days-old pups and 90-days-old mature rats that had been weaned early (day 16), normally (day 21), or late (day 26). Early weaning resulted in deeper crypts, lower villous/crypt ratio, and a smaller villous area on day 21. Crown-tail length correlated positively with the crypt depth and negatively with the villous/crypt ratio. At age 90 days, early weaned animals had shallower crypts, a greater villous/crypt ratio, and a smaller villous area compared with their normally weaned counterparts. The rats' crown-tail length correlated negatively with the crypt depth and positively with the villous/crypt ratio. Early weaning significantly affects the intestinal mucosa, which may impact food absorption and lead to differences in somatic growth compared with late weaning. Over time there may be a phase of compensation with increased villus height and crypt depth.
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Affiliation(s)
- Yonatan Crispel
- Neurophysiology Unit, Rambam Health Care Campus, Haifa, Israel.,Rappaport Family Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
| | - Ron Shaoul
- Rappaport Family Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel.,Pediatric Gastroenterology and Nutrition Institute, Ruth's Children's Hospital of Haifa, Rambam Medical Center, POB 9602, Haifa 31096, Israel
| | - Ranya Khamaise
- Rappaport Family Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
| | - Edmond Sabo
- Rappaport Family Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel.,Pathology Institute, Rambam Health Care Campus, Haifa, Israel
| | - Ze'ev Hochberg
- Rappaport Family Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 31096, Israel
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