Abstract
Introduction: Alpha angle (AA) is a widely used measure of hip shape that is commonly used to define cam morphology, a bulging of the lateral aspect of the femoral head. Cam morphology has shown strong associations with hip osteoarthritis (OA) making the AA a clinically relevant measure. In both clinical practice and research studies, AA tends to be measured manually which can be inconsistent and time-consuming.
Objective: We aimed to (i) develop an automated method of deriving AA from anterior-posterior dual-energy x-ray absorptiometry (DXA) scans; and (ii) validate this method against manual measures of AA.
Methods: 6,807 individuals with left hip DXAs were selected from UK Biobank. Outline points were manually placed around the femoral head on 1,930 images before training a Random Forest-based algorithm to place the points on a further 4,877 images. An automatic method for calculating AA was written in Python 3 utilising these outline points. An iterative approach was taken to developing and validating the method, testing the automated measures against independent batches of manually measured images in sequential experiments.
Results: Over the course of six experimental stages the concordance correlation coefficient, when comparing the automatic AA to manual measures of AA, improved from 0.28 [95% confidence interval 0.13-0.43] for the initial version to 0.88 [0.84-0.92] for the final version. The inter-rater kappa statistic comparing automatic versus manual measures of cam morphology, defined as AA ³≥60°, improved from 0.43 [80% agreement] for the initial version to 0.86 [94% agreement] for the final version.
Conclusions: We have developed and validated an automated measure of AA from DXA scans, showing high agreement with manually measuring AA. The proposed method is available to the wider research community from Zenodo.
Objective: We aimed to (i) develop an automated method of deriving AA from anterior-posterior dual-energy x-ray absorptiometry (DXA) scans; and (ii) validate this method against manual measures of AA.
Methods: 6,807 individuals with left hip DXAs were selected from UK Biobank. Outline points were manually placed around the femoral head on 1,930 images before training a Random Forest-based algorithm to place the points on a further 4,877 images. An automatic method for calculating AA was written in Python 3 utilising these outline points. An iterative approach was taken to developing and validating the method, testing the automated measures against independent batches of manually measured images in sequential experiments.
Results: Over the course of six experimental stages the concordance correlation coefficient, when comparing the automatic AA to manual measures of AA, improved from 0.28 [95% confidence interval 0.13-0.43] for the initial version to 0.88 [0.84-0.92] for the final version. The inter-rater kappa statistic comparing automatic versus manual measures of cam morphology, defined as AA ³≥60°, improved from 0.43 [80% agreement] for the initial version to 0.86 [94% agreement] for the final version.
Conclusions: We have developed and validated an automated measure of AA from DXA scans, showing high agreement with manually measuring AA. The proposed method is available to the wider research community from Zenodo.
| Original language | English |
|---|---|
| Number of pages | 11 |
| Journal | Wellcome open research |
| Volume | 6 |
| Issue number | 60 |
| DOIs | |
| Publication status | Published - 15 Mar 2021 |
Bibliographical note
Grant information: RE, MF, FS are supported by, and this work is funded by a Wellcome Trust collaborative award (209233). BGF is supported by a Medical Research Council (MRC) clinical research training fellowship (MR/S021280/1). BGF, MF, JHT, GDS work in the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the MRC (MC_UU_00011/1). CL was funded by the MRC, UK (MR/S00405X/1).Keywords
- Alpha Angle
- DXA
- Automation
- Cam
- Femoro-acetabular impingement