Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period. Issue 4 (April 2020)

• Record Type: Journal Article
• Authors: Guan, B.; Liu, F.; Haj-Mirzaian, A.; Demehri, S.; Samsonov, A.; Neogi, T.; Guermazi, A.; Kijowski, R.
• Is Part Of: Osteoarthritis and cartilage. Volume 28:Issue 4(2020)
• Page Start: 428
• Page End: 437
• Publication Date: 2020-04
• Subjects: Osteoarthritis -- Deep learning -- Radiographs -- Risk assessment models; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; Arthrose -- Périodiques; Articulations -- Maladies -- Périodiques; 616.7223005
• DOI: 10.1016/j.joca.2020.01.010
• Languages: English
• ISSNs: 1063-4584

Abstract

Summary: Objective: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays. Methods: Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance. Results: The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher ( P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL ( P = 0.015) and traditional ( P < 0.001) models. Conclusion: DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.