Osteoporosis is a common disease that increases fracture risk. Hip fractures,
especially in elderly people, lead to increased morbidity, decreased quality of
life and increased mortality. Being a silent disease before fracture,
osteoporosis often remains undiagnosed and untreated. Areal bone mineral
density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the
gold-standard method for osteoporosis diagnosis and hence also for future
fracture prediction (prognostic). However, the required special equipment is
not broadly available everywhere, in particular not to patients in developing
countries. We propose a deep learning classification model (FORM) that can
directly predict hip fracture risk from either plain radiographs (X-ray) or 2D
projection images of computed tomography (CT) data. Our method is fully
automated and therefore well suited for opportunistic screening settings,
identifying high risk patients in a broader population without additional
screening. FORM was trained and evaluated on X-rays and CT projections from the
Osteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or
2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that
FORM can correctly predict the 10-year hip fracture risk with a validation AUC
of 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional
information like age, BMI, fall history and health background across a 5-fold
cross validation on the X-ray and CT cohort, respectively. Our approach
significantly (p < 0.01) outperforms previous methods like Cox
Proportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21
respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD
based predictions. We are confident that FORM can contribute on improving
osteoporosis diagnosis at an early stage.Comment: Accepted at MICCAI 2022 Workshop (PRIME