Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning

Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study Publisher

Daskareh M¹ ; Vakilpour A² ; Barzegargolmoghani E³ ; Esmaeilian S⁴ ; Gilanchi S⁵ ; Ezzati F⁶ ; Alikhani M⁷ ; Rahmanipour E⁸ ; Amini N⁷ ; Ghorbani M⁹ ; Pezeshk P¹⁰

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1. Department of Radiology, University of California San Diego, San Diego, 92093, CA, United States
2. Division of Cardiovascular Diseases, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, 19104, PA, United States
3. Department of Biomedical Engineering, Tarbiat Modares University, Tehran, 14115-111, Iran
4. Department of Radiology, Shiraz University of Medical Sciences, Shiraz, 71348, Iran
5. Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, 19839-63113, Iran
6. Division of Rheumatic Disease, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, 75390, TX, United States
7. Department of Internal Medicine, Rheumatology Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, 14117-13135, Iran
8. Immunology Research Center, Mashhad University of Medical Sciences, Mashhad, 91779-48564, Iran
9. Orthopedic Research Center, Mashhad University of Medical Sciences, Mashhad, 91779-48564, Iran
10. Division of Musculoskeletal Imaging, Department of Radiology, UT Southwestern Medical Center, Dallas, 75390, TX, United States

Source: Diagnostics Published:2024

Abstract

Background: The early diagnosis and treatment of rheumatoid arthritis (RA) are essential to prevent joint damage and enhance patient outcomes. Diagnosing RA in its early stages is challenging due to the nonspecific and variable clinical signs and symptoms. Our study aimed to identify the most predictive features of hand ultrasound (US) for RA development and assess the performance of machine learning models in diagnosing preclinical RA. Methods: We conducted a prospective cohort study with 326 adults who had experienced hand joint pain for less than 12 months and no clinical arthritis. We assessed the participants clinically and via hand US at baseline and followed them for 24 months. Clinical progression to RA was defined according to the ACR/EULAR criteria. Regression modeling and machine learning approaches were used to analyze the predictive US features. Results: Of the 326 participants (45.10 ± 11.37 years/83% female), 123 (37.7%) developed clinical RA during follow-up. At baseline, 84.6% of the progressors had US synovitis, whereas 16.3% of the non-progressors did (p < 0.0001). Only 5.7% of the progressors had positive PD. Multivariate analysis revealed that the radiocarpal synovial thickness (OR = 39.8), PIP/MCP synovitis (OR = 68 and 39), and wrist effusion (OR = 12.56) on US significantly increased the odds of developing RA. ML confirmed these US features, along with the RF and anti-CCP levels, as the most important predictors of RA. Conclusions: Hand US can identify preclinical synovitis and determine the RA risk. The radiocarpal synovial thickness, PIP/MCP synovitis, wrist effusion, and RF and anti-CCP levels are associated with RA development. © 2024 by the authors.

Style	Citing Format
MLA	Daskareh M, et al.. "Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study." Diagnostics, vol. 14, no. 11, 2024, pp. -.
APA	Daskareh M, Vakilpour A, Barzegargolmoghani E, Esmaeilian S, Gilanchi S, Ezzati F, Alikhani M, Rahmanipour E, Amini N, Ghorbani M, Pezeshk P (2024). Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study. Diagnostics, 14(11), -.
Chicago	Daskareh M, Vakilpour A, Barzegargolmoghani E, Esmaeilian S, Gilanchi S, Ezzati F, Alikhani M, et al.. "Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study." Diagnostics 14, no. 11 (2024): -.
Harvard	Daskareh M et al. (2024) 'Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study', Diagnostics, 14(11), pp. -.
Vancouver	Daskareh M, Vakilpour A, Barzegargolmoghani E, Esmaeilian S, Gilanchi S, Ezzati F, et al.. Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study. Diagnostics. 2024;14(11):-.
BibTex	@article{ author = {Daskareh M and Vakilpour A and Barzegargolmoghani E and Esmaeilian S and Gilanchi S and Ezzati F and Alikhani M and Rahmanipour E and Amini N and Ghorbani M and Pezeshk P}, title = {Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study}, journal = {Diagnostics}, volume = {14}, number = {11}, pages = {-}, year = {2024} }
RIS	TY - JOUR AU - Daskareh M AU - Vakilpour A AU - Barzegargolmoghani E AU - Esmaeilian S AU - Gilanchi S AU - Ezzati F AU - Alikhani M AU - Rahmanipour E AU - Amini N AU - Ghorbani M AU - Pezeshk P TI - Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study JO - Diagnostics VL - 14 IS - 11 SP - EP - PY - 2024 ER -

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