The Value of Artificial Intelligence in Predicting Perioperative Complications and Key Quality Metrics Following Elective Degenerative Spine Surgery: A Narrative Review of the Current Landscape.

STUDY

DESIGN

Narrative review.

OBJECTIVE

To perform a review discussing current applications of artificial intelligence (AI) and machine learning (ML) in the prediction of quality metrics in elective degenerative spinal surgeries.

SUMMARY OF BACKGROUND DATA: A major barrier to the long-term effectiveness of elective spine surgery is the high incidence of perioperative complications. The use of AI and ML in the preoperative evaluation of elective spine surgery has been limited.

METHODS

A literature review was performed to identify adults who underwent surgery for elective lumbar degenerative spinal pathology or deformity. Studies were stratified by cohort size, type of complications that the AI/ML models predicted, type of machine learning algorithm, and performance of AI/ML models.

RESULTS

Included for analysis were 46 studies. The median study sample size to build and validate predictive models was 4538 (109-279,135). Models and associated AUCs are as follows: AUC 0.57-0.95 [extended length of stay (LOS), discharge disposition, and costs], AUC 0.59-0.95 (medical complications), and AUC 0.64-0.87 (short-term readmissions/reoperations).

CONCLUSIONS

The AI/ML models favored supervised learning algorithms and opened the possibility for additional development of unsupervised and reinforcement-based algorithms. Future models should utilize additional granular predictors such as the surgical invasiveness index, social support, and socioeconomic variables to enhance predictive capabilities.