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Hartmann Tolić I, Habijan M, Galić I, Nyarko EK. Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review. Biomimetics (Basel) 2024; 9:493. [PMID: 39194472 DOI: 10.3390/biomimetics9080493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/06/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024] Open
Abstract
Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide, though it often goes undiagnosed. The early and accurate diagnosis of celiac disease (CD) is critical to preventing severe health complications, with computer-aided diagnostic approaches showing significant promise. However, there is a shortage of review literature that encapsulates the field's current state and offers a perspective on future advancements. Therefore, this review critically assesses the literature on the role of imaging techniques, biomarker analysis, and computer models in improving CD diagnosis. We highlight the diagnostic strengths of advanced imaging and the non-invasive appeal of biomarker analyses, while also addressing ongoing challenges in standardization and integration into clinical practice. Our analysis stresses the importance of computer-aided diagnostics in fast-tracking the diagnosis of CD, highlighting the necessity for ongoing research to refine these approaches for effective implementation in clinical settings. Future research in the field will focus on standardizing CAD protocols for broader clinical use and exploring the integration of genetic and protein data to enhance early detection and personalize treatment strategies. These advancements promise significant improvements in patient outcomes and broader implications for managing autoimmune diseases.
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Affiliation(s)
- Ivana Hartmann Tolić
- Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia
| | - Marija Habijan
- Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia
| | - Irena Galić
- Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia
| | - Emmanuel Karlo Nyarko
- Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University, 31000 Osijek, Croatia
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2
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Zulqarnain F, Zhao X, Setchell KD, Sharma Y, Fernandes P, Srivastava S, Shrivastava A, Ehsan L, Jain V, Raghavan S, Moskaluk C, Haberman Y, Denson LA, Mehta K, Iqbal NT, Rahman N, Sadiq K, Ahmad Z, Idress R, Iqbal J, Ahmed S, Hotwani A, Umrani F, Amadi B, Kelly P, Brown DE, Moore SR, Ali SA, Syed S. Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction. iScience 2024; 27:110013. [PMID: 38868190 PMCID: PMC11167436 DOI: 10.1016/j.isci.2024.110013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/18/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.
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Affiliation(s)
| | - Xueheng Zhao
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Kenneth D.R. Setchell
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Yash Sharma
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | - Varun Jain
- University of Virginia, Charlottesville, VA, USA
| | | | | | - Yael Haberman
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Lee A. Denson
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Khyati Mehta
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | | | | | | | | | | | | | | | | | | | | | - Paul Kelly
- University Teaching Hospital, Lusaka, Zambia
- Queen Mary University of London, London, UK
| | | | | | | | - Sana Syed
- University of Virginia, Charlottesville, VA, USA
- Aga Khan University, Karachi, Pakistan
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3
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Yilmaz F, Brickman A, Najdawi F, Yakirevich E, Egger R, Resnick MB. Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies. J Transl Med 2024; 104:102043. [PMID: 38431118 DOI: 10.1016/j.labinv.2024.102043] [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: 10/30/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024] Open
Abstract
This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.
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Affiliation(s)
- Fazilet Yilmaz
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Arlen Brickman
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Fedaa Najdawi
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Evgeny Yakirevich
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Murray B Resnick
- The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island.
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4
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Gruver AM, Lu H, Zhao X, Fulford AD, Soper MD, Ballard D, Hanson JC, Schade AE, Hsi ED, Gottlieb K, Credille KM. Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response. Diagn Pathol 2023; 18:122. [PMID: 37951937 PMCID: PMC10638821 DOI: 10.1186/s13000-023-01412-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. METHODS A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. RESULTS Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. CONCLUSIONS Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.
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Affiliation(s)
- Aaron M Gruver
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Haiyan Lu
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Xiaoxian Zhao
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Angie D Fulford
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Michael D Soper
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Darryl Ballard
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Jeffrey C Hanson
- Research Informatics, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Andrew E Schade
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Eric D Hsi
- Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
| | - Klaus Gottlieb
- Immunology, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Kelly M Credille
- Clinical Diagnostics Laboratory, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA.
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Babcock SJ, Flores-Marin D, Thiagarajah JR. The genetics of monogenic intestinal epithelial disorders. Hum Genet 2023; 142:613-654. [PMID: 36422736 PMCID: PMC10182130 DOI: 10.1007/s00439-022-02501-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 10/23/2022] [Indexed: 11/27/2022]
Abstract
Monogenic intestinal epithelial disorders, also known as congenital diarrheas and enteropathies (CoDEs), are a group of rare diseases that result from mutations in genes that primarily affect intestinal epithelial cell function. Patients with CoDE disorders generally present with infantile-onset diarrhea and poor growth, and often require intensive fluid and nutritional management. CoDE disorders can be classified into several categories that relate to broad areas of epithelial function, structure, and development. The advent of accessible and low-cost genetic sequencing has accelerated discovery in the field with over 45 different genes now associated with CoDE disorders. Despite this increasing knowledge in the causal genetics of disease, the underlying cellular pathophysiology remains incompletely understood for many disorders. Consequently, clinical management options for CoDE disorders are currently limited and there is an urgent need for new and disorder-specific therapies. In this review, we provide a general overview of CoDE disorders, including a historical perspective of the field and relationship to other monogenic disorders of the intestine. We describe the genetics, clinical presentation, and known pathophysiology for specific disorders. Lastly, we describe the major challenges relating to CoDE disorders, briefly outline key areas that need further study, and provide a perspective on the future genetic and therapeutic landscape.
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Affiliation(s)
- Stephen J Babcock
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Enders Rm 605, 300 Longwood Ave, Boston, MA, 02115, USA
| | - David Flores-Marin
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Enders Rm 605, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Jay R Thiagarajah
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Enders Rm 605, 300 Longwood Ave, Boston, MA, 02115, USA.
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Cowardin CA, Syed S, Iqbal N, Jamil Z, Sadiq K, Iqbal J, Ali SA, Moore SR. Environmental enteric dysfunction: gut and microbiota adaptation in pregnancy and infancy. Nat Rev Gastroenterol Hepatol 2023; 20:223-237. [PMID: 36526906 PMCID: PMC10065936 DOI: 10.1038/s41575-022-00714-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/16/2022] [Indexed: 03/31/2023]
Abstract
Environmental enteric dysfunction (EED) is a subclinical syndrome of intestinal inflammation, malabsorption and barrier disruption that is highly prevalent in low- and middle-income countries in which poverty, food insecurity and frequent exposure to enteric pathogens impair growth, immunity and neurodevelopment in children. In this Review, we discuss advances in our understanding of EED, intestinal adaptation and the gut microbiome over the 'first 1,000 days' of life, spanning pregnancy and early childhood. Data on maternal EED are emerging, and they mirror earlier findings of increased risks for preterm birth and fetal growth restriction in mothers with either active inflammatory bowel disease or coeliac disease. The intense metabolic demands of pregnancy and lactation drive gut adaptation, including dramatic changes in the composition, function and mother-to-child transmission of the gut microbiota. We urgently need to elucidate the mechanisms by which EED undermines these critical processes so that we can improve global strategies to prevent and reverse intergenerational cycles of undernutrition.
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Affiliation(s)
- Carrie A Cowardin
- Division of Paediatric Gastroenterology, Hepatology and Nutrition, Department of Paediatrics, Child Health Research Center, University of Virginia, Charlottesville, VA, USA
| | - Sana Syed
- Division of Paediatric Gastroenterology, Hepatology and Nutrition, Department of Paediatrics, Child Health Research Center, University of Virginia, Charlottesville, VA, USA
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Najeeha Iqbal
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zehra Jamil
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Kamran Sadiq
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Junaid Iqbal
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Syed Asad Ali
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Sean R Moore
- Division of Paediatric Gastroenterology, Hepatology and Nutrition, Department of Paediatrics, Child Health Research Center, University of Virginia, Charlottesville, VA, USA.
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7
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Faust O, De Michele S, Koh JE, Jahmunah V, Lih OS, Kamath AP, Barua PD, Ciaccio EJ, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107320. [PMID: 36608429 DOI: 10.1016/j.cmpb.2022.107320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP). METHODS Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images. RESULTS For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC. CONCLUSIONS To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers.
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Affiliation(s)
| | - Simona De Michele
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - Joel Ew Koh
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - V Jahmunah
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | | | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Edward J Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Suzanne K Lewis
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Peter H Green
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Govind Bhagat
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA; Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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9
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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10
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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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11
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The global burden of coeliac disease: opportunities and challenges. Nat Rev Gastroenterol Hepatol 2022; 19:313-327. [PMID: 34980921 DOI: 10.1038/s41575-021-00552-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 02/06/2023]
Abstract
Coeliac disease is a systemic disorder characterized by immune-mediated enteropathy, which is caused by gluten ingestion in genetically susceptible individuals. The clinical presentation of coeliac disease is highly variable and ranges from malabsorption through solely extra-intestinal manifestations to asymptomatic. As a result, the majority of patients with coeliac disease remain undiagnosed, misdiagnosed or experience a substantial delay in diagnosis. Coeliac disease is diagnosed by a combination of serological findings of disease-related antibodies and histological evidence of villous abnormalities in duodenal biopsy samples. However, variability in histological grading and in the diagnostic performance of some commercially available serological tests remains unacceptably high and confirmatory assays are not readily available in many parts of the world. Currently, the only effective treatment for coeliac disease is a lifelong, strict, gluten-free diet. However, many barriers impede patients' adherence to this diet, including lack of widespread availability, high cost, cross-contamination and its overall restrictive nature. Routine follow-up is necessary to ensure adherence to a gluten-free diet but considerable variation is evident in follow-up protocols and the optimal disease management strategy is not clear. However, these challenges in the diagnosis and management of coeliac disease suggest opportunities for future research.
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12
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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13
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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: 35] [Impact Index Per Article: 17.5] [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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14
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Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World J Gastroenterol 2022; 28:449-463. [PMID: 35125829 PMCID: PMC8790554 DOI: 10.3748/wjg.v28.i4.449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/16/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
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15
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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16
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Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021; 31:743-758. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA
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17
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Chen D, Fulmer C, Gordon IO, Syed S, Stidham RW, Vande Casteele N, Qin Y, Falloon K, Cohen BL, Wyllie R, Rieder F. Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know. J Crohns Colitis 2021; 16:460-471. [PMID: 34558619 PMCID: PMC8919817 DOI: 10.1093/ecco-jcc/jjab169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.
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Affiliation(s)
- David Chen
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Clifton Fulmer
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Ilyssa O Gordon
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Sana Syed
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA,School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Ryan W Stidham
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | - Yi Qin
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Katherine Falloon
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Benjamin L Cohen
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Robert Wyllie
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Pediatric Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Florian Rieder
- Corresponding author: Florian Rieder, MD, Department of Inflammation and Immunity, and Department of Gastroenterology, Hepatology, & Nutrition, Cleveland Clinic Foundation, 9500 Euclid Ave., Cleveland, OH 44195, USA. Tel: (216) 445-5631; Fax: (216) 636-0104; E-mail:
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18
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Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
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Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
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19
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Syed S, Ehsan L, Shrivastava A, Sengupta S, Khan M, Kowsari K, Guleria S, Sali R, Kant K, Kang SJ, Sadiq K, Iqbal NT, Cheng L, Moskaluk CA, Kelly P, Amadi BC, Ali SA, Moore SR, Brown DE. Artificial Intelligence-based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection. J Pediatr Gastroenterol Nutr 2021; 72:833-841. [PMID: 33534362 PMCID: PMC8767179 DOI: 10.1097/mpg.0000000000003057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; environmental enteropathy (EE) and celiac disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. METHODS Data for the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.
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Affiliation(s)
- Sana Syed
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Lubaina Ehsan
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Aman Shrivastava
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
- Data Science Institute, University of Virginia, Charlottesville, VA
| | - Saurav Sengupta
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
- Data Science Institute, University of Virginia, Charlottesville, VA
| | - Marium Khan
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Kamran Kowsari
- Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
- University of California Los Angeles, Los Angeles, CA, USA
| | - Shan Guleria
- School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Rasoul Sali
- Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
| | - Karan Kant
- Data Science Institute, University of Virginia, Charlottesville, VA
| | - Sung-Jun Kang
- Data Science Institute, University of Virginia, Charlottesville, VA
| | - Kamran Sadiq
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Najeeha T. Iqbal
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Lin Cheng
- Pathology Department, Rush University Medical Center, Chicago, IL, USA
| | | | - Paul Kelly
- Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine, Lusaka, Zambia
- Blizard Institute, Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Beatrice C. Amadi
- Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine, Lusaka, Zambia
| | - S. Asad Ali
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Sean R. Moore
- Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Donald E. Brown
- Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
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20
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Koh JEW, De Michele S, Sudarshan VK, Jahmunah V, Ciaccio EJ, Ooi CP, Gururajan R, Gururajan R, Oh SL, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106010. [PMID: 33831693 DOI: 10.1016/j.cmpb.2021.106010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villous atrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification. METHODS The Steerable Pyramid Transform (SPT) method was used to obtain sub bands from which various types of entropy and nonlinear features were computed. All extracted features were automatically classified into two-class and multi-class, using six classifiers. RESULTS An accuracy of 88.89%, was achieved for the classification of two-class villous abnormalities based on analysis of Hematoxylin and Eosin (H&E) stained biopsy images. Similarly, an accuracy of 82.92% was achieved for the two-class classification of red-green-blue (RGB) biopsy images. Also, an accuracy of 72% was achieved in the classification of multi-class biopsy images. CONCLUSION The results obtained are promising, and demonstrate the possibility of automating biopsy image interpretation using machine learning. This can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy, and ultimately, earlier access to treatment.
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Affiliation(s)
- Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Simona De Michele
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - Vidya K Sudarshan
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Edward J Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Raj Gururajan
- School of Business, University of Southern Queensland Springfield, Australia
| | | | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Suzanne K Lewis
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Peter H Green
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Govind Bhagat
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA; Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business, University of Southern Queensland Springfield, Australia; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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21
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Ludwig T, Oukid I, Wong J, Ting S, Huysentruyt K, Roy P, Foussat AC, Vandenplas Y. Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers. J Pediatr Gastroenterol Nutr 2021; 72:255-261. [PMID: 33275399 PMCID: PMC7815249 DOI: 10.1097/mpg.0000000000003007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND/AIMS Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML). METHODS In total, 2687 usable smartphone photos of diapers with stool from 96 children younger than 24 months were obtained after independent ethical study approval. Stool consistency was assessed from each photo according to the original 7 types of the Brussels Infant and Toddler Stool Scale independently by study participants and 2 researchers. A health care professional assigned a final score in case of scoring disagreement between the researchers. A proof-of-concept ML model was built upon this collected photo database, using transfer learning to re-train the classification layer of a pretrained deep convolutional neural network model. The model was built on random training (n = 2478) and test (n = 209) subsets. RESULTS Agreements between study participants and both researchers were 58.0% and 48.5%, respectively, and between researchers 77.5% (assessable n = 2366). The model classified 60.3% of the test photos in exact agreement with the final score. With respect to the 4-class grouping of the 7 Brussels Infant and Toddler Stool Scale types, the agreement between model-based and researcher classification was 77.0%. CONCLUSION The automated and objective scoring of stool consistency from diaper photos by the ML model shows robust agreement with human raters and overcomes limitations of other methods relying on caregiver reporting. Integrated with a smartphone application, this new framework for photo database construction and ML classification has numerous potential applications in clinical studies and home assessment.
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Affiliation(s)
- Thomas Ludwig
- Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore
| | | | - Jill Wong
- Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore
| | - Steven Ting
- Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore
| | - Koen Huysentruyt
- KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Puspita Roy
- Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore
| | - Agathe C. Foussat
- Danone Nutricia Research, Precision Nutrition D-lab, Biopolis, Singapore
| | - Yvan Vandenplas
- KidZ Health Castle, UZ Brussel, Vrije Universiteit Brussel, Brussels, Belgium
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22
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Sana MK, Hussain ZM, Shah PA, Maqsood MH. Artificial intelligence in celiac disease. Comput Biol Med 2020; 125:103996. [PMID: 32979542 DOI: 10.1016/j.compbiomed.2020.103996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022]
Abstract
Celiac disease (CD) has been on the rise in the world and a large part of it remains undiagnosed. Novel methods are required to address the gaps in prompt detection and management. Artificial intelligence (AI) has seen an exponential surge in the last decade worldwide. With the advent of big data and powerful computational ability, we now have self-driving cars and smart devices in our daily lives. Huge databases in the form of electronic medical records and images have rendered healthcare a lucrative sector where AI can prove revolutionary. It is being used extensively to overcome the barriers in clinical workflows. From the perspective of a disease, it can be deployed in multiple steps i.e. screening tools, diagnosis, developing novel therapeutic agents, proposing management plans, and defining prognostic indicators, etc. We review the areas where it may augment physicians in the delivery of better healthcare by summarizing current literature on the use of AI in healthcare using CD as a model. We further outline major barriers to its large-scale implementations and prospects from the healthcare point of view.
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Affiliation(s)
- Muhammad Khawar Sana
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
| | - Zeshan M Hussain
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, United States.
| | - Pir Ahmad Shah
- Department of Internal Medicine, University of Texas Health Science Center, San Antonio, TX, 78229, United States.
| | - Muhammad Haisum Maqsood
- Department of Internal Medicine, King Edward Medical University, Mayo Hospital Lahore, Lahore, Punjab, 54000, Pakistan.
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23
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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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Balaban DV, Jinga M. Digital histology in celiac disease: A practice changer. Artif Intell Gastroenterol 2020; 1:1-4. [DOI: 10.35712/aig.v1.i1.1] [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: 07/01/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has grown tremendously in the last decades and is undoubtedly the future era in medicine. Concerning digestive diseases, applications of AI include clinical gastroenterology, gastrointestinal endoscopy and imaging, and not least pathological diagnosis. Several gastrointestinal pathologies require histological confirmation for a positive diagnosis. Among them, celiac disease (CD) diagnosis has been in the spotlight over time, but controversy is still ongoing with regard to the so-called celiac-type histology. Despite efforts to improve histological diagnosis in CD, there are still several issues and pitfalls associated with duodenal histology reading. Several papers have assessed the accuracy of AI techniques in detecting CD on duodenal biopsy images and have shown high diagnostic performance over standard histology reading. We discuss the role of computer-assisted histology in improving the assessment of mucosal architectural injury and inflammation in CD patients, both for diagnosis and follow-up.
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Affiliation(s)
- Daniel Vasile Balaban
- Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest 020021, Romania
| | - Mariana Jinga
- Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest 020021, Romania
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Digital histology in celiac disease: A practice changer. Artif Intell Gastroenterol 2020. [DOI: 10.35712/wjg.v1.i1.1] [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: 02/06/2023] Open
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Stidham RW. Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology. Gastroenterol Hepatol (N Y) 2020; 16:341-349. [PMID: 34035738 PMCID: PMC8132644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, reproducibility, and, potentially, insight that may exceed that of human medical specialists. AI is powered by computational methods that allow machines to replicate clinical pattern recognition used by gastroenterology specialists to interpret endoscopic or cross-sectional images; understand the meaning and intent of medical documents; and merge different types of data to infer a diagnosis, prognosis, or expected outcome. Ongoing research is studying the use of AI for automated interpretation of text from colonoscopy and clinical documents for improved quality and patient phenotyping as well as enhanced detection and descriptions of polyps and other endoscopic lesions, and for predicting the probability of future therapeutic response early in a treatment course. This article introduces emerging technologies of natural language processing, machine vision, and machine learning for data analytics, and describes current and future applications in gastroenterology.
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Affiliation(s)
- Ryan W Stidham
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
- Morphomics Analysis Program, University of Michigan, Ann Arbor, Michigan
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Liu TC, VanBuskirk K, Ali SA, Kelly MP, Holtz LR, Yilmaz OH, Sadiq K, Iqbal N, Amadi B, Syed S, Ahmed T, Moore S, Ndao IM, Isaacs MH, Pfeifer JD, Atlas H, Tarr PI, Denno DM, Moskaluk CA. A novel histological index for evaluation of environmental enteric dysfunction identifies geographic-specific features of enteropathy among children with suboptimal growth. PLoS Negl Trop Dis 2020; 14:e0007975. [PMID: 31929525 PMCID: PMC6980693 DOI: 10.1371/journal.pntd.0007975] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 01/24/2020] [Accepted: 12/06/2019] [Indexed: 02/06/2023] Open
Abstract
Background A major limitation to understanding the etiopathogenesis of environmental enteric dysfunction (EED) is the lack of a comprehensive, reproducible histologic framework for characterizing the small bowel lesions. We hypothesized that the development of such a system will identify unique histology features for EED, and that some features might correlate with clinical severity. Methods Duodenal endoscopic biopsies from two cohorts where EED is prevalent (Pakistan, Zambia) and North American children with and without gluten sensitive enteropathy (GSE) were processed for routine hematoxylin & eosin (H&E) staining, and scanned to produce whole slide images (WSIs) which we shared among study pathologists via a secure web browser-based platform. A semi-quantitative scoring index composed of 11 parameters encompassing tissue injury and response patterns commonly observed in routine clinical practice was constructed by three gastrointestinal pathologists, with input from EED experts. The pathologists then read the WSIs using the EED histology index, and inter-observer reliability was assessed. The histology index was further used to identify within- and between-child variations as well as features common across and unique to each cohort, and those that correlated with host phenotype. Results Eight of the 11 histologic scoring parameters showed useful degrees of variation. The overall concordance across all parameters was 96% weighted agreement, kappa 0.70, and Gwet’s AC 0.93. Zambian and Pakistani tissues shared some histologic features with GSE, but most features were distinct, particularly abundance of intraepithelial lymphocytes in the Pakistani cohort, and marked villous destruction and loss of secretory cell lineages in the Zambian cohort. Conclusions We propose the first EED histology index for interpreting duodenal biopsies. This index should be useful in future clinical and translational studies of this widespread, poorly understood, and highly consequential disorder, which might be caused by multiple contributing processes, in different regions of the world. The study of EED has been limited by the lack of a rigorously tested, reproducible histology index that can provide insight to the pathogenesis of this entity. In this study we report the first duodenal histology index that was developed using an unbiased approach, with excellent inter-observer reproducibility, for the study of EED. The EED histology index readily identified histologic features that are common or unique to cohorts of distinct geographic locations. Incorporating the histology index into future clinical studies will provide useful insight into the pathogenesis and for intervention strategy development.
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Affiliation(s)
- Ta-Chiang Liu
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Kelley VanBuskirk
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Syed A. Ali
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - M. Paul Kelly
- Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine, Lusaka, Zambia
- Blizard Institute, Barts & The London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Lori R. Holtz
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Omer H. Yilmaz
- The David H. Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, Department of Pathology, Massachusetts General Hospital, Boston, MA, United States of America
| | - Kamran Sadiq
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Najeeha Iqbal
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Beatrice Amadi
- Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine, Lusaka, Zambia
| | - Sana Syed
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics, University of Virginia, Charlottesville, VA, United States of America
| | - Tahmeed Ahmed
- Nutrition and Clinical Services Division (NCSD), International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
- James P. Grant School of Public Health, BRAC University, Dhaka, Bangladesh
| | - Sean Moore
- Department of Pediatrics, University of Virginia, Charlottesville, VA, United States of America
| | - I. Malick Ndao
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Michael H. Isaacs
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - John D. Pfeifer
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Hannah Atlas
- Departments of Pediatrics and Global Health, University of Washington, Seattle, WA, United States of America
| | - Phillip I. Tarr
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Donna M. Denno
- Departments of Pediatrics and Global Health, University of Washington, Seattle, WA, United States of America
| | - Christopher A. Moskaluk
- Department of Pathology, University of Virginia, Charlottesville, VA, United States of America
- * E-mail:
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Abstract
Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.
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Error in Funding/Support. JAMA Netw Open 2019; 2:e197967. [PMID: 31268538 PMCID: PMC6613285 DOI: 10.1001/jamanetworkopen.2019.7967] [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: 11/14/2022] Open
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