Explore how the cutting-edge development and validation of an artificial intelligence model is revolutionizing the prediction of spinopelvic parameters, setting new standards in neurosurgery.
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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 without manual input. 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). A test set of 40 radiographs was evaluated by four expert reviewers to assess SpinePose‘s accuracy, which showed median errors within a minimal range for each parameter (e.g., SVA 2.2 mm, PT 1.3°). The tool demonstrated excellent reliability (ICC 0.91-1.0) across all parameters, matching the reliability of fellowship-trained spine surgeons and neuroradiologists.
Significance: SpinePose addresses the challenges of time-intensive and variable reliability in measuring spinopelvic parameters by offering a rapid, consistent, and highly accurate AI solution. This advancement could significantly enhance patient selection and surgical planning in spinal treatments, marking a notable contribution to the field of spinal imaging and surgery.