Dive into the future of spinal neurosurgery with our latest blog post on the groundbreaking development and validation of an artificial intelligence model designed to revolutionize the prediction of spinopelvic parameters.
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Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.
Harake et al., J Neurosurg Spine 2024
<!– DOI: 10.3171/2024.1.SPINE231252 //–>
https://doi.org/10.3171/2024.1.SPINE231252
The study introduces SpinePose, an innovative artificial intelligence (AI) tool designed to automatically predict spinopelvic parameters accurately, eliminating the need for manual data entry. This tool was trained and validated on 761 sagittal whole-spine radiographs, focusing on parameters such as the 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).
Testing on a separate set of 40 radiographs and comparing the results to those of experienced reviewers, including spine surgeons and a neuroradiologist, SpinePose demonstrated high accuracy with median errors for parameters like SVA at 2.2 mm, PT at 1.3°, and LL at 2.6°. The tool also showed excellent reliability across all parameters with intraclass correlation coefficients (ICCs) ranging from 0.91 to 1.0.
This advancement is significant as it suggests that AI can match the expertise of fellowship-trained professionals in predicting spinopelvic alignment, which is crucial for patient selection and surgical planning. The efficiency and consistency offered by SpinePose could revolutionize spinal imaging and treatment planning.