Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters
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| Format: | Preprint |
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2024
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| _version_ | 1866914672080322560 |
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| author | Harake, Edward S. Linzey, Joseph R. Jiang, Cheng Joshi, Rushikesh S. Zaki, Mark M. Jones, Jaes C. Khalsa, Siri S. Lee, John H. Wilseck, Zachary Joseph, Jacob R. Hollon, Todd C. Park, Paul |
| author_facet | Harake, Edward S. Linzey, Joseph R. Jiang, Cheng Joshi, Rushikesh S. Zaki, Mark M. Jones, Jaes C. Khalsa, Siri S. Lee, John H. Wilseck, Zachary Joseph, Jacob R. Hollon, Todd C. Park, Paul |
| contents | Objective. Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited by some degree of manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry.
Methods. SpinePose was trained and validated on 761 sagittal whole-spine X-rays to predict sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1-pelvic angle (T1PA), and L1-pelvic angle (L1PA). A separate test set of 40 X-rays was labeled by 4 reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability.
Results. SpinePose exhibited the following median (interquartile range) parameter errors: SVA: 2.2(2.3)mm, p=0.93; PT: 1.3(1.2)°, p=0.48; SS: 1.7(2.2)°, p=0.64; PI: 2.2(2.1)°, p=0.24; LL: 2.6(4.0)°, p=0.89; T1PA: 1.1(0.9)°, p=0.42; and L1PA: 1.4(1.6)°, p=0.49. Model predictions also exhibited excellent reliability at all parameters (ICC: 0.91-1.0).
Conclusions. SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06185 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters Harake, Edward S. Linzey, Joseph R. Jiang, Cheng Joshi, Rushikesh S. Zaki, Mark M. Jones, Jaes C. Khalsa, Siri S. Lee, John H. Wilseck, Zachary Joseph, Jacob R. Hollon, Todd C. Park, Paul Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Objective. Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited by some degree of manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry. Methods. SpinePose was trained and validated on 761 sagittal whole-spine X-rays to predict sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1-pelvic angle (T1PA), and L1-pelvic angle (L1PA). A separate test set of 40 X-rays was labeled by 4 reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability. Results. SpinePose exhibited the following median (interquartile range) parameter errors: SVA: 2.2(2.3)mm, p=0.93; PT: 1.3(1.2)°, p=0.48; SS: 1.7(2.2)°, p=0.64; PI: 2.2(2.1)°, p=0.24; LL: 2.6(4.0)°, p=0.89; T1PA: 1.1(0.9)°, p=0.42; and L1PA: 1.4(1.6)°, p=0.49. Model predictions also exhibited excellent reliability at all parameters (ICC: 0.91-1.0). Conclusions. SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning. |
| title | Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.06185 |