The goal of this study is to assess the performance of an artificial intelligence software (Osteo Signal) in detecting osteoporosis risk in adults 50 years and older. The main question it aims to answer is: What is the accuracy of the software in detecting osteoporosis risk on chest x-ray images as compared to the standard technique of dual-energy x-ray absorptiometry (DXA)? There is no direct involvement of participants in this study as it will use data from individuals who have already had a chest x-ray and a DXA scan taken in the past.
Study Type
OBSERVATIONAL
Enrollment
595
Artificial Intelligence (AI) software
Erasmus University Medical Center Rotterdam,
Rotterdam, Netherlands
NOT_YET_RECRUITINGMid and South Essex NHS Foundation Trust
Westcliff-on-Sea, Essex, United Kingdom
NOT_YET_RECRUITINGBarts Health NHS Trust
London, United Kingdom
RECRUITINGTwo class bone status classification (osteoporosis/non osteoporosis)
Device output against the lowest DXA as measured by sensitivity, specificity, and area under the ROC curve (AUC)
Time frame: At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other
Three class bone status classification (osteoporosis/osteopenia/normal)
Device output against the lowest DXA as measured by area under the ROC curve (AUC), overall per cent agreement, and weighted Cohen's kappa
Time frame: At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other
Classification for osteoporosis and osteopenia
Positive predictive value (PPV) and negative predictive value (NPV) for osteoporosis and osteopenia classifications
Time frame: At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other
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