Machine learning is increasingly being explored in spine surgery to predict patient outcomes, assist surgical planning and improve intraoperative decision-making, according to a review in the fall 2026 issue of Vertebral Columns, published by the International Society for the Advancement of Spine Surgery.
The review, authored by researchers affiliated with Corewell Health William Beaumont University Hospital, Oakland University William Beaumont School of Medicine and Atlas Spine+Brain in Royal Oak, Mich., examines current applications of machine learning in spine surgery and barriers to broader clinical adoption.
Five takeaways from the review:
- Predicting surgical risks. Machine learning models are being developed to estimate postoperative complications, hospital length of stay, readmissions and functional outcomes. The authors identified tree-based models as the most common model category for preoperative decision-making.
- Identifying surgical candidates. Researchers are using clinical, demographic and imaging data to predict which patients may benefit more from surgery than conservative treatment.
- Supporting surgical planning. Automated imaging analysis can identify vertebrae, neural structures and surrounding tissues. Studies cited in the review found machine learning-based segmentation could reduce planning time while maintaining accuracy.
- Monitoring patients during surgery. Machine learning is being investigated to interpret neuromonitoring signals, identify potential neurologic injuries and anticipate complications.
- Tracking recovery. Models using imaging, wearable devices and other patient data are being studied to monitor postoperative recovery and identify deviations from expected progress.
The authors cautioned that limited external validation, inconsistent reporting of patient-reported outcomes, difficulties interpreting model predictions and patient privacy concerns remain barriers to widespread adoption.
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