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Deep learning approach to predict pain progression in knee osteoarthritis

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Abstract

Objective

To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA).

Materials and methods

The incidence and progression cohorts of the Osteoarthritis Initiative, a multi-center longitudinal study involving 9348 knees in 4674 subjects with or at risk of knee OA that began in 2004 and is ongoing, were used to conduct this retrospective analysis. A subset of knees without and with pain progression (defined as a 9-point or greater increase in pain score between baseline and two or more follow-up time points over the first 48 months) was randomly stratified into training (4200 knees with a mean age of 61.0 years and 60% female) and hold-out testing (500 knees with a mean age of 60.8 years and 60% female) datasets. A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors. A combined model was developed to combine demographic, clinical, and radiographic risk factors with DL analysis of baseline knee radiographs. Area under the curve (AUC) analysis was performed using the hold-out testing dataset to evaluate model performance.

Results

The traditional model had an AUC of 0.692 (66.9% sensitivity and 64.1% specificity). The DL model had an AUC of 0.770 (76.7% sensitivity and 70.5% specificity), which was significantly higher (p < 0.001) than the traditional model. The combined model had an AUC of 0.807 (72.3% sensitivity and 80.9% specificity), which was significantly higher (p < 0.05) than the traditional and DL models.

Conclusions

DL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors.

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Funding

Funding support for the manuscript was provided by the National Institute of Arthritis and Musculoskeletal and Skin Disease R01-AR068373-01 grant.

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Authors and Affiliations

  1. Department of Radiology, University of Wisconsin, 1111 Highland Avenue, Madison, WI, 53705-2275, USA

    Bochen Guan & Alexey Samsonov

  2. Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA

    Bochen Guan

  3. Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, MA, USA

    Fang Liu

  4. Department of Radiology, Johns Hopkins University, Baltimore, MD, USA

    Arya Haj Mizaian & Shadpour Demehri

  5. Department of Radiology, Boston University, Boston, MA, USA

    Ali Guermazi

  6. Department of Radiology, New York University, New York, NY, USA

    Richard Kijowski

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  1. Bochen Guan
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  2. Fang Liu
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Correspondence to Bochen Guan.

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Guan, B., Liu, F., Mizaian, A.H. et al. Deep learning approach to predict pain progression in knee osteoarthritis. Skeletal Radiol 51, 363–373 (2022). https://doi.org/10.1007/s00256-021-03773-0

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  • DOI: https://doi.org/10.1007/s00256-021-03773-0

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