# Compression Fractures Detection on CT

arXiv: https://arxiv.org/abs/1706.01671 · HTML: https://mlsanity.com/paper/1706.01671/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization](https://mlsanity.com/paper/2204.06818/) (2022)
- [Interpretable Vertebral Fracture Diagnosis](https://mlsanity.com/paper/2203.16273/) (2022)
- [Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading](https://mlsanity.com/paper/2303.12031/) (2023)
- [Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes](https://mlsanity.com/paper/2404.02830/) (2024)
- [An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in CT Scans via Neurosymbolic Modeling](https://mlsanity.com/paper/2412.17258/) (2024)

## Abstract (arXiv)

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.