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Article

Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients

1
MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
2
Data Science Institute, London School of Economics and Political Science, London, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Bioengineering 2024, 11(8), 824; https://doi.org/10.3390/bioengineering11080824 (registering DOI)
Submission received: 4 July 2024 / Revised: 28 July 2024 / Accepted: 30 July 2024 / Published: 12 August 2024

Abstract

Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687–0.781) and 0.747 (95% CI 0.701–0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features.
Keywords: knee osteoarthritis; WOMAC; machine learning; imaging; radiograph; MRI knee osteoarthritis; WOMAC; machine learning; imaging; radiograph; MRI

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MDPI and ACS Style

Nair, A.; Alagha, M.A.; Cobb, J.; Jones, G. Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients. Bioengineering 2024, 11, 824. https://doi.org/10.3390/bioengineering11080824

AMA Style

Nair A, Alagha MA, Cobb J, Jones G. Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients. Bioengineering. 2024; 11(8):824. https://doi.org/10.3390/bioengineering11080824

Chicago/Turabian Style

Nair, Abhinav, M. Abdulhadi Alagha, Justin Cobb, and Gareth Jones. 2024. "Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients" Bioengineering 11, no. 8: 824. https://doi.org/10.3390/bioengineering11080824

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