Summary of An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in Ct Scans Via Neurosymbolic Modeling, by Blanca Inigo et al.
An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in CT Scans via Neurosymbolic Modeling
by Blanca Inigo, Yiqing Shen, Benjamin D. Killeen, Michelle Song, Axel Krieger, Christopher Bradley, Mathias Unberath
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a neurosymbolic approach for detecting vertebral compression fractures (VCFs) in CT scans, which is essential for diagnosing undiagnosed VCFs resulting from osteoporosis. The proposed model combines deep learning (DL) for vertebral segmentation with a shape-based algorithm (SBA) to analyze vertebral height distributions. This approach provides interpretability by defining a rule set over the height distributions to detect VCFs, which can enhance clinician’s trust and support informed decision-making in diagnosis and treatment planning. The method achieves high accuracy and sensitivity on the VerSe19 dataset, matching or surpassing the performance of black box deep neural networks. This work demonstrates the potential of intrinsically explainable models for VCF detection, particularly in high-stakes scenarios like opportunistic medical diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to detect a type of bone fracture called vertebral compression fractures (VCFs). These fractures often go undiagnosed and can be serious if left untreated. The researchers developed a new way to analyze CT scans to find these fractures, which they call the neurosymbolic approach. It’s like having a special set of rules that helps doctors understand why the AI is saying there might be a fracture. This approach is good because it’s easy for doctors to understand and trust what the AI is telling them. The new method worked well on a test dataset and could help doctors make better decisions about treating VCFs. |
Keywords
» Artificial intelligence » Deep learning
