1. Identifying Scoliosis in Population-Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X-ray Absorptiometry Scans
- Author
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Jeremy Fairbank, Amir Jamaludin, Tim J Peters, Ian Harding, Emma Clark, Andrew Zisserman, and Timor Kadir
- Subjects
medicine.medical_specialty ,Computer science ,Endocrinology, Diabetes and Metabolism ,030209 endocrinology & metabolism ,Scoliosis ,Population based ,Bristol DXA scoliosis method ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Endocrinology ,Machine learning ,medicine ,Orthopedics and Sports Medicine ,Medical physics ,030212 general & internal medicine ,Dual-energy X-ray absorptiometry ,Original Research ,medicine.diagnostic_test ,business.industry ,Total body ,Bristol DXA Scoliosis method ,ALSPAC ,medicine.disease ,Automation ,Section (archaeology) ,business - Abstract
Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes. However, to allow full utilisation of population-based research cohorts, this needs to be automated. The purpose of this study was therefore to automate the identification of spinal curvature from total body DXA scans using machine learning techniques. To validate the automation, we assessed: (1) sensitivity, specificity and area under the receiver operator curve value (AUC) by comparison with 12,000 manually annotated images; (2) reliability by rerunning the automation on a subset of DXA scans repeated 2–6 weeks apart and calculating the kappa statistic; (3) validity by applying the automation to 5000 non-annotated images to assess associations with epidemiological variables. The final automated model had a sensitivity of 86.5%, specificity of 96.9% and an AUC of 0.80 (95%CI 0.74–0.87). There was almost perfect agreement of identification of those with scoliosis (kappa 0.90). Those with scoliosis identified by the automated model showed similar associations with gender, ethnicity, socioeconomic status, BMI and lean mass to previous literature. In conclusion, we have developed an accurate and valid automated method for identifying and quantifying spinal curvature from total body DXA scans.
- Published
- 2020
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