[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116932-en":3,"doc-seo-116932-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116932,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification","Antibody testing in inflammatory bowel disease (IBD) supports diagnostic accuracy for Crohn’s disease (CD) and ulcerative colitis (UC), while the value of machine learning for finer subtype distinctions remains uncertain. This study analyzed antibody profiles of 100 adult IBD patients with known CD or UC and 76 IBD-unclassified (IBD-U) patients from the Swiss IBD cohort. ASCA IgG/IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA were used to build antibody panels and machine learning models. An optimized panel separated CD from UC with an AUC of 85%, but no specific antibodies predicted IBD-U or reclassification, and supervised models failed to distinguish CD, UC, and IBD-U. Unsupervised modeling suggested only two clusters, indicating limited subtype refinement for IBD-U.","source: [https://doi.org/10.48350/185159 | downloaded:](https://doi.org/10.48350/185159 | downloaded:) 7.8.2023  \nArticle  \nMachine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classifi cation  \nChristiane Sokollik 1,†, Aurélie Pahud de Mortanges 2,†, Alexander B. Leichtle 3,4, Pascal Juillerat 5,6 and Michael P. Horn 3,* on behalf of the Swiss IBD Cohort Study Group  \nCitation: Sokollik, C.;  \nPahud de Mortanges, A.;  \nLeichtle, A.B.; Juillerat, P.;  \nHorn, M.P., on behalf of the Swiss IBD Cohort Study Group. Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification. Diagnostics 2023, 13, 2491. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/diagnostics13152491](10.3390/diagnostics13152491)  \nAcademic Editor: Consolato M. Sergi  \nReceived: 19 June 2023  \nRevised: 21 July 2023  \nAccepted: 25 July 2023  \nPublished: 26 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://cre](https://cre)[ativecommons.org/licenses/by/4.0/](ativecommons.org/licenses/by/4.0/)).  \n1 Division of Pediatric Gastroenterology, Hepatology and Nutrition, University Children’s Hospital, Inselspital, University of Bern, 3010 Bern, Switzerland; christiane.sokollik@insel.ch  \n2 ARTORG Center for Biomedical Engineering Research, University of Bern, 3010 Bern, Switzerland; [aurelie.pahuddemortanges@unibe.ch](aurelie.pahuddemortanges@unibe.ch)  \n3 Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland; alexander.leichtle@insel.ch  \n4 Center for Artificial Intelligence in Medicine (CAIM), University of Bern, 3010 Bern, Switzerland  \n5 Department of Gastroenterology, Clinic for Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland  \n6 Crohn’s and Colitis Center, Gastroenterology Beaulieu SA, 1004 Lausanne, Switzerland; pjuillerat@gesb.ch  \n* Correspondence: michael.horn@insel.ch; Tel.: +41-31-632-32-19 † These authors contributed equally to this work.  \nAbstract: Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn’s disease (CD) and ulcerative colitis (UC) . Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO-and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85%(95%CI, 78–92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2–73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD.  \nKeywords: Crohn’s disease; ulcerative colitis; PR3-ANCA; serology; ASCA  \n1. Introduction  \nThe term inflammatory bowel disease (IBD) summarizes a spectrum of chronic diseases characterized by recurrent episodes of intestinal inflammation. There are two main subtypes: Crohn’s disease (CD) and ulcerative colitis (","cbCaiahuUofJdFaX","https://ap.wps.com/l/cbCaiahuUofJdFaX","pdf",1466801,1,13,"English","en",105,"# Abstract\n# Introduction\n## IBD subtypes and the role of antibody testing\n# Materials and Methods\n## Study population and design","[{\"question\":\"How was the antibody profile for IBD subtypes determined in this study?\",\"answer\":\"The study used ASCA IgG/IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements to compute antibody panels and train machine learning models.\"},{\"question\":\"How well did the optimized antibody panel distinguish Crohn’s disease from ulcerative colitis?\",\"answer\":\"The optimized antibody panel achieved an AUC of 85% (95% CI, 78–92%) to distinguish CD from UC.\"},{\"question\":\"Did machine learning models improve prediction of IBD-unclassified (IBD-U) or reclassification?\",\"answer\":\"No. Supervised machine learning could not distinguish CD, UC, and IBD-U, and specific antibody profiles did not predict IBD-U or reclassification during follow-up.\"}]","Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification | 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was the antibody profile for IBD subtypes determined in this study?","Question",{"text":75,"@type":76},"The study used ASCA IgG/IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements to compute antibody panels and train machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How well did the optimized antibody panel distinguish Crohn’s disease from ulcerative colitis?",{"text":80,"@type":76},"The optimized antibody panel achieved an AUC of 85% (95% CI, 78–92%) to distinguish CD from UC.",{"name":82,"@type":73,"acceptedAnswer":83},"Did machine learning models improve prediction of IBD-unclassified (IBD-U) or reclassification?",{"text":84,"@type":76},"No. Supervised machine learning could not distinguish CD, UC, and IBD-U, and specific antibody profiles did not predict IBD-U or reclassification during 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