On-line First

J Rheum Dis

Published online January 20, 2025

© Korean College of Rheumatology

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human

Jucheol Moon, Ph.D.1 , Pratik Jadhav, M.S.1 , Sangtae Choi, M.D., Ph.D.2

1Department of Computer Engineering and Computer Science, College of Engineering, California State University Long Beach, Long Beach, CA, USA, 2Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Correspondence to : Sangtae Choi, https://orcid.org/0000-0002-2074-1733
Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, 102 Heukseokro, Dongjak-gu, Seoul 06973, Korea. E-mail: beconst@cau.ac.kr

Received: November 4, 2024; Revised: December 13, 2024; Accepted: December 29, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL’s applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.

Keywords Deep learning, Artificial intelligence, Rheumatology, Diagnostic imaging

Article

On-line First

J Rheum Dis

Published online January 20, 2025

Copyright © Korean College of Rheumatology.

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human

Jucheol Moon, Ph.D.1 , Pratik Jadhav, M.S.1 , Sangtae Choi, M.D., Ph.D.2

1Department of Computer Engineering and Computer Science, College of Engineering, California State University Long Beach, Long Beach, CA, USA, 2Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Correspondence to:Sangtae Choi, https://orcid.org/0000-0002-2074-1733
Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, 102 Heukseokro, Dongjak-gu, Seoul 06973, Korea. E-mail: beconst@cau.ac.kr

Received: November 4, 2024; Revised: December 13, 2024; Accepted: December 29, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL’s applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.

Keywords: Deep learning, Artificial intelligence, Rheumatology, Diagnostic imaging

JRD
Jan 01, 2025 Vol.32 No.1, pp. 1~7
COVER PICTURE
Cumulative growth of rheumatology members and specialists (1980~2024). Cumulative distribution of the number of the (A) Korean College of Rheumatology members and (B) rheumatology specialists. (J Rheum Dis 2025;32:63-65)

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