Artificial intelligence system for predicting areal bone mineral density from plain X-rays.
Authors
Affiliations (5)
Affiliations (5)
- School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
- Saigon Precision Medicine Research Center, Ho Chi Minh City, Vietnam.
- BioMedical Research Center, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam.
- School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia. [email protected].
- School of Population Health, UNSW Sydney, Sydney, Australia. [email protected].
Abstract
Dual-energy X-ray absorptiometry (DXA) is the standard method for assessing areal bone mineral density (aBMD), diagnosing osteoporosis, and predicting fracture risk. However, DXA's availability is limited in resource-poor areas. This study aimed to develop an artificial intelligence (AI) system capable of estimating aBMD from standard radiographs. The study was part of the Vietnam Osteoporosis Study, a prospective population-based research involving 3783 participants aged 18 years and older. A total of 7060 digital radiographs of the frontal pelvis and lateral spine were taken using the FCR Capsula XLII system (Fujifilm Corp., Tokyo, Japan). aBMD at the femoral neck and lumbar spine was measured with DXA (Hologic Horizon, Hologic Corp., Bedford, MA, USA). An ensemble of seven deep-learning models was used to analyze the X-rays and predict bone mineral density, termed "xBMD". The correlation between xBMD and aBMD was evaluated using Pearson's correlation coefficients. The correlation between xBMD and aBMD at the femoral neck was strong ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90; 95% CI, 0.88-0.91), and similarly high at the lumbar spine ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.87; 95% CI, 0.85-0.88). This correlation remained consistent across different age groups and genders. The AI system demonstrated excellent performance in identifying individuals at high risk for hip fractures, with area under the ROC curve (AUC) values of 0.96 (95% CI, 0.95-0.98) at the femoral neck and 0.97 (95% CI, 0.96-0.99) at the lumbar spine. These findings indicate that AI can accurately predict aBMD and identify individuals at high risk of fractures. This AI system could provide an efficient alternative to DXA for osteoporosis screening in settings with limited resources and high patient demand. An AI system developed to predict aBMD from X-rays showed strong correlations with DXA ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90 at femoral neck; = <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> 0.87 at lumbar spine) and high accuracy in identifying individuals at high risk for fractures (AUC = 0.96 at femoral neck; AUC = 0.97 at lumbar spine).