B Guan1, F Liu2, A Haj-Mirzaian3, S Demehri4, A Samsonov5, T Neogi6, A Guermazi7, R Kijowski8. 1. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA; Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA. Electronic address: gbochen@wisc.edu. 2. Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, MA, USA. Electronic address: fliu12@mgh.harvard.edu. 3. Department of Radiology, Johns Hopkins University, Baltimore, MD, USA. Electronic address: arya.mirzaian@gmail.com. 4. Department of Radiology, Johns Hopkins University, Baltimore, MD, USA. Electronic address: demehri2001@yahoo.com. 5. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. Electronic address: samsonov@wisc.edu. 6. Department of Medicine, Boston University, Boston, MA, USA. Electronic address: tneogi@bu.edu. 7. Department of Radiology, Boston University, Boston, MA, USA. Electronic address: guermazi@bu.edu. 8. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. Electronic address: rkijowski@uwhealth.org.
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.
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.
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