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.

本研究使用手腕和手腕 X 光图像进行骨质疏松症预测，为增加筛查率而不增加成本或时间提供了一种新的方法。通过图像分割和自监督学习，本方法实现了对骨质疏松症的有效分类，为从外周骨骼部位利用视觉技术进行骨质疏松症识别的先导性工作。

利用图像分割与自监督学习预测手腕 X 射线片中的骨质疏松症

Osteoporosis Prediction from Hand and Wrist X-rays using Image  Segmentation and Self-Supervised Learning

Osteoporosis is a prevalent bone disease that causes fractures in fragile
bones, leading to a decline in daily living activities. Dual-energy X-ray
absorptiometry (DXA) and quantitative computed tomography (QCT) are highly
accurate for diagnosing osteoporosis; however, these modalities require special
equipment and scan protocols. To frequently monitor bone health, low-cost,
low-dose, and ubiquitously available diagnostic methods are highly anticipated.
In this study, we aim to perform bone mineral density (BMD) estimation from a
plain X-ray image for opportunistic screening, which is potentially useful for
early diagnosis. Existing methods have used multi-stage approaches consisting
of extraction of the region of interest and simple regression to estimate BMD,
which require a large amount of training data. Therefore, we propose an
efficient method that learns decomposition into projections of bone-segmented
QCT for BMD estimation under limited datasets. The proposed method achieved
high accuracy in BMD estimation, where Pearson correlation coefficients of
0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD
estimation tasks, respectively, and the root mean square of the coefficient of
variation values were 3.27 to 3.79% for four measurements with different poses.
Furthermore, we conducted extensive validation experiments, including
multi-pose, uncalibrated-CT, and compression experiments toward actual
application in routine clinical practice.

本文提出了一种有效的骨密度估计方法，通过对限定数据集进行骨分割 QCT 的分解投影学习，实现了高准确性的骨密度估计，适用于机会性筛查和早期诊断。