Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period

• Published in: Osteoarthritis and cartilage 2020-04, Vol.28 (4), p.428-437
• Main Authors: Guan, B., Liu, F., Haj-Mirzaian, A., Demehri, S., Samsonov, A., Neogi, T., Guermazi, A., Kijowski, R.
• Format: Article
• Language: English
• Subjects: Aged; Area Under Curve; Deep Learning; Disease Progression; Female; Humans; Male; Middle Aged; Osteoarthritis; Osteoarthritis, Knee - diagnostic imaging; Osteoarthritis, Knee - epidemiology; Osteoarthritis, Knee - physiopathology; Radiographs; Radiography; Risk Assessment; Risk assessment models; Risk Factors
• ISSN: 1063-4584; 1522-9653
• DOI: 10.1016/j.joca.2020.01.010

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. 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. 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