[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124946-en":3,"doc-seo-124946-105":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},124946,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Fluorescence optical imaging feature selection with machine learning for differential diagnosis of selected rheumatic diseases","Accurate, rapid diagnosis of hand-affecting rheumatic diseases is essential for treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced microcirculation impairment through increased signal intensity, generating multiple image features. This study identifies FOI features that improve differential diagnosis among rheumatoid arthritis, osteoarthritis, and connective tissue diseases by applying statistical and machine-learning feature-ranking and stepwise feature subset selection to maximize diagnostic accuracy.","TYPE Original Research PUBLISHED 21 August 2023  \nDOI 10. 3389/fmed.2023.1228833  \nOPEN ACCESS  \nEDITED BY  \nSara Moccia,  \nSant’Anna School of Advanced Studies, Italy  \nREVIEWED BY  \nSara Mazzucato,  \nSant’Anna School of Advanced Studies, Italy Maria Chiara Fiorentino,  \nMarche Polytechnic University, Italy  \n*CORRESPONDENCE  \nEgbert Gedat  \n [gedat@th-wildau.de](gedat@th-wildau.de)  \nRECEIVED 25 May 2023  \nACCEPTED 28 July 2023  \nPUBLISHED 21 August 2023  \nCITATION  \nRothe F, Berger J, Welker P, Fiebelkorn R, Kupper S, Kiesel D, Gedat E and Ohrndorf S (2023) Fluorescence optical imaging featureselection with machine learning for di􀀀erential diagnosis of selected rheumatic diseases.  \nFront. Med. 10:1228833 .  \ndoi: 10.3389/fmed.2023.1228833  \nCOPYRIGHT  \n© 2023 Rothe, Berger, Welker, Fiebelkorn, Kupper, Kiesel, Gedat and Ohrndorf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nFluorescence optical imaging feature selection with machine learning for di􀀀erential diagnosis of selected rheumatic diseases  \nFelix Rothe1 , Jörn Berger2 , Pia Welker3 , Richard Fiebelkorn1 , Stefan Kupper1 , Denise Kiesel3 , Egbert Gedat1,2* and  \nSarah Ohrndorf4  \n1Telematics Research Group, Wildau Technical University of Applied Sciences, Wildau, Germany, 2Xiralite GmbH, Berlin, Germany, 3 Institute of Functional Anatomy, Charité—Universitätsmedizin Berlin, Berlin, Germany, 4 Department of Rheumatology and Clinical Immunology, Charité—Universitätsmedizin Berlin, Berlin, Germany  \nBackground and objective: Accurate and fast diagnosis of rheumatic diseases a􀀀ecting the hands is essential for further treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced impaired microcirculation by increasing signal intensity, resulting in di􀀀erent image features. This analysis aimed to find specific image features in FOI that might be important for accurately diagnosing di􀀀erent rheumatic diseases.  \nPatients and methods: FOI images of the hands of patients with di􀀀erent types of rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD), were assessed in a reading of 20 di􀀀erent image features in three phases of the contrast agent dynamics, yielding 60 di􀀀erent features for each patient. The readings were analyzed for mutual di􀀀erential diagnosis of the three diseases (One-vs-One) and each disease in all data (One-vs-Rest) . In the first step, statistical tools and machine-learning-based methods were applied to reveal the importance rankings of the features, that is, to find features that contribute most to the model-based classification. In the second step machine learning with a stepwise increasing number of features was applied, sequentially adding at each step the most crucial remaining feature to extract a minimized subset that yields the highest diagnostic accuracy.  \nResults: In total, n = 605 FOI of both hands were analyzed (n = 235 with RA, n = 229 with OA, and n = 141 with CTD) . All classification problems showed maximum accuracy with a reduced set of image features. For RA-vs. -OA, five features were needed for high accuracy. For RA-vs. -CTD ten, OA-vs. -CTD sixteen, RAvs. -Rest five, OA-vs. -Rest eleven, and CTD-vs-Rest fifteen, features were needed, respectively. For all problems, the final importance ranking of the features with respect to the contrast agent dynamics was determined.  \nConclusions: With the presented investigations, the set of features in FOI examinations relevant to the di􀀀erential diagnosis of the selected rheumatic diseases could be remarkably reduced, provid","cbCairKQopgs8ZAY","https://ap.wps.com/l/cbCairKQopgs8ZAY","pdf",4548100,1,12,"English","en",105,"# Background and objective\n# Patients and methods\n## Feature assessment and analysis strategy\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To identify specific FOI image features that could be important for accurately differentiating selected rheumatic diseases affecting the hands.\"},{\"question\":\"Which diseases were compared using fluorescence optical imaging?\",\"answer\":\"Rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD) were evaluated for mutual differential diagnosis.\"},{\"question\":\"How were the most relevant image features selected for diagnosis?\",\"answer\":\"The study first used statistical and machine-learning methods to rank feature importance, then applied machine learning with a stepwise increasing number of features to extract a minimized subset achieving the highest diagnostic accuracy.\"}]","Fluorescence optical imaging feature selection with machine learning for differential diagnosis of selected rheumatic diseases | 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