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

arXiv: https://arxiv.org/abs/2311.06834 · HTML: https://mlsanity.com/paper/2311.06834/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)

## Built on by (most cited first, 3 of 3)

- [RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis](https://mlsanity.com/paper/2507.05193/) (2025)
- [Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification](https://mlsanity.com/paper/2505.22551/) (2025)
- [RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis](https://mlsanity.com/paper/2605.05616/) (2026)

## Abstract (arXiv)

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.