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Osteoporosis is a common disease that increases fracture risk. Hip fractures,especially in elderly people, lead to increased morbidity, decreased quality oflife and increased mortality. Being a silent disease before fracture,osteoporosis often remains undiagnosed and untreated. Areal bone mineraldensity (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is thegold-standard method for osteoporosis diagnosis and hence also for futurefracture prediction (prognostic). However, the required special equipment isnot broadly available everywhere, in particular not to patients in developingcountries. We propose a deep learning classification model (FORM) that candirectly predict hip fracture risk from either plain radiographs (X-ray) or 2Dprojection images of computed tomography (CT) data. Our method is fullyautomated and therefore well suited for opportunistic screening settings,identifying high risk patients in a broader population without additionalscreening. FORM was trained and evaluated on X-rays and CT projections from theOsteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show thatFORM can correctly predict the 10-year hip fracture risk with a validation AUCof 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additionalinformation like age, BMI, fall history and health background across a 5-foldcross validation on the X-ray and CT cohort, respectively. Our approachsignificantly (p < 0.01) outperforms previous methods like CoxProportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMDbased predictions. We are confident that FORM can contribute on improvingosteoporosis diagnosis at an early stage.

Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study