Osteoporosis Prediction from Hand and Wrist X-rays using Image
  Segmentation and Self-Supervised Learning
Hyungeun Lee Ung Hwang Seungwon Yu Chang-Hun Lee Kijung Yoon
Osteoporosis is a widespread and chronic metabolic bone disease that often
remains undiagnosed and untreated due to limited access to bone mineral density
(BMD) tests like Dual-energy X-ray absorptiometry (DXA). In response to this
challenge, current advancements are pivoting towards detecting osteoporosis by
examining alternative indicators from peripheral bone areas, with the goal of
increasing screening rates without added expenses or time. In this paper, we
present a method to predict osteoporosis using hand and wrist X-ray images,
which are both widely accessible and affordable, though their link to DXA-based
data is not thoroughly explored. Initially, our method segments the ulnar,
radius, and metacarpal bones using a foundational model for image segmentation.
Then, we use a self-supervised learning approach to extract meaningful
representations without the need for explicit labels, and move on to classify
osteoporosis in a supervised manner. Our method is evaluated on a dataset with
192 individuals, cross-referencing their verified osteoporosis conditions
against the standard DXA test. With a notable classification score (AUC=0.83),
our model represents a pioneering effort in leveraging vision-based techniques
for osteoporosis identification from the peripheral skeleton sites.