Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Lisa C Adams; Keno K Bressem; Katharina Ziegeler; Janis L Vahldiek; Denis Poddubnyy

doi:10.1016/j.jbspin.2023.105651

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Joint Bone Spine. 2023 Oct 4;91(3):105651. doi: 10.1016/j.jbspin.2023.105651. Online ahead of print.

Authors

Lisa C Adams¹, Keno K Bressem², Katharina Ziegeler³, Janis L Vahldiek⁴, Denis Poddubnyy⁵

Affiliations

¹ Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany. Electronic address: lisa.adams@tum.de.
² Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
³ Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany.
⁴ Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany.
⁵ Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany.

PMID: 37797827
DOI: 10.1016/j.jbspin.2023.105651

Abstract

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Keywords: Artificial intelligence; Disease diagnosis and management; Magnetic resonance imaging; Rheumatology.