Summary of Development and Validation Of An Artificial Intelligence Model to Accurately Predict Spinopelvic Parameters, by Edward S. Harake et al.
Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters
by Edward S. Harake, Joseph R. Linzey, Cheng Jiang, Rushikesh S. Joshi, Mark M. Zaki, Jaes C. Jones, Siri S. Khalsa, John H. Lee, Zachary Wilseck, Jacob R. Joseph, Todd C. Hollon, Paul Park
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed AI tool, SpinePose, automates the measurement of spinopelvic radiographic parameters to achieve accurate and rapid alignment assessments. By leveraging machine learning techniques, SpinePose eliminates the need for manual user-entry requirements, improving interobserver reliability and reducing the time-intensive nature of traditional methods. This innovative approach has significant implications for clinical practice, enabling more effective symptom management and treatment planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI tool called SpinePose helps doctors measure spinopelvic alignment quickly and accurately without needing people to manually enter information. This is important because proper alignment can make a big difference in how patients feel. Right now, measuring alignment takes a long time and different doctors might get slightly different results. SpinePose promises to solve these problems by using artificial intelligence to analyze X-rays and provide precise measurements. |
Keywords
* Artificial intelligence * Alignment * Machine learning
