A Meaningful Journey to Predict Fractures with Deep Learning.

Abstract

Osteoporotic fractures worsen patients’ quality of life and increase the mortality rate [1]. The mortality rate within the first 12 months after a hip fracture in Koreans aged 50 years or older was 14.0% for women and 21.0% for men [2]. Osteoporosis is an in-evitable consequence of aging, but fracture prediction is the start-ing point to preventing fractures; therefore, accurate fracture prediction is more important than ever. Although lower bone miner-al density (BMD) increases the risk of fracture, fractures also oc-cur in patients with less marked reduced bone mass. In Korea, the 10-year cumulative incidence of fragility fractures was 31.1% and 37.5% in postmenopausal women with normal BMD and osteopenia, respectively, whereas it was 44.3% in women with osteoporosis [3]. Therefore, BMD measurements and bone quality should be reflected in evaluations of bone strength. analyzing the microstructure of high-resolution peripheral (TBS) indirect indicator of bone microarchitecture. evaluates bone quality based on the information obtained by lumbar dual-energy