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

B. Guan, F. Liu, A. Haj-Mirzaian, S. Demehri, A. Samsonov, T. Neogi, A. Guermazi, R. Kijowski

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)428-437
Number of pages10
JournalOsteoarthritis and Cartilage
Volume28
Issue number4
DOIs
StatePublished - Apr 2020

Keywords

  • Deep learning
  • Osteoarthritis
  • Radiographs
  • Risk assessment models

ASJC Scopus subject areas

  • Rheumatology
  • Biomedical Engineering
  • Orthopedics and Sports Medicine

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