Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors

Shengyu Lu, Nicholas R. Fuggle, Leo D. Westbury, Mícheál Ó Breasail, Gregorio Bevilacqua, Kate A. Ward, Elaine M. Dennison, Sasan Mahmoodi, Mahesan Niranjan, Cyrus Cooper

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Background: Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is ‘read’ by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk. Methods: Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods. Results: Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56–0.84), which improved to 0.71 (0.57–0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83–0.96) with a sensitivity of 0.83 and specificity of 0.74. Conclusion: These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.

Original languageEnglish
Article number116653
Number of pages7
Publication statusPublished - Mar 2023
Externally publishedYes


  • Bone microarchitecture
  • Computer vision
  • Fracture risk
  • High-resolution peripheral quantitative computed tomography (HR-pQCT)
  • Machine learning
  • Osteoporosis (OP)

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