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Peer-Reviewed Publication
Diagnostics (Basel)2025;15(12)June 9, 2025Journal Article

Deep Learning Based Automatic Ankle Tenosynovitis Quantification from MRI in Patients with Psoriatic Arthritis: A Feasibility Study.

Saeed Arbabi1,2, Vahid Arbabi2,3, Lorenzo Costa1,2, Iris Ten Katen4, Simon C Mastbergen5, Peter R Seevinck1,6, Pim A de Jong4, Harrie Weinans2,7, Mylène P Jansen5, Wouter Foppen4
1Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
2Department of Orthopedics, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
3Orthopaedic-Biomechanics Research Group, Department of Mechanical Engineering, Faculty of Engineering, Birjand 561, Iran.
4Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
5Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
6MRIguidance B.V., 3584 CX Utrecht, The Netherlands.
7Department of Biomechanical Engineering, Delft University of Technology (TU Delft), 2628 CD Delft, The Netherlands.
PubMed PMC DOI PDF

Abstract

Background/Objectives: Tenosynovitis is a common feature of psoriatic arthritis (PsA) and is typically assessed using semi-quantitative magnetic resonance imaging (MRI) scoring. However, visual scoring s variability. This study evaluates a fully automated, deep-learning approach for ankle tenosynovitis segmentation and volume-based quantification from MRI in psoriatic arthritis (PsA) patients. Met…

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