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Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review.

Cesar D Lopez¹, Venkat Boddapati¹, Joseph M Lombardi¹

¹Department of Orthopaedic Surgery, The Spine Hospital, 21611New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.

Global Spine Journal

|March 1, 2022

Summary

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Artificial intelligence and machine learning (AI/ML) show promise in spine surgery for patient selection and predicting costs and length of stay. However, AI/ML models are less accurate for predicting postoperative complications and readmissions.

Area of Science:

Spine Surgery
Artificial Intelligence
Machine Learning
Health Informatics

Background:

Spine surgery is increasingly utilizing advanced computational tools.
Optimizing patient selection and predicting outcomes are critical challenges in spine surgery.
The application of AI/ML in spine surgery is rapidly expanding.

Purpose of the Study:

To systematically review and evaluate research on AI/ML applications in spine surgery.
To assess AI/ML's role in preoperative patient selection.
To analyze AI/ML's effectiveness in predicting and managing postoperative outcomes and complications.

Main Methods:

Comprehensive literature search of EMBASE, Medline, and PubMed databases.

Keywords:

artificial intelligence deep learning machine learning orthopedic surgery predictive modeling spine surgery

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Inclusion of all research-based studies regardless of evidence level or publication date.

Reporting of findings according to PRISMA guidelines.

Main Results:

41 studies were included in the review.
Bayesian networks achieved the highest average AUC (.80), while neural networks showed the best accuracy (83.0%).
AI/ML models excelled in preoperative planning, cost prediction, and length of stay prediction, but were less accurate for readmissions/reoperations.

Conclusions:

AI/ML presents a valuable tool for optimizing spine surgery patient care and cost-efficiency.
AI/ML models demonstrate superior performance in preoperative optimization and length of stay prediction.
Further development is needed to improve AI/ML accuracy in predicting postoperative complications and readmissions in spine surgery.