An Introduction to Machine Learning for the Practicing Spine Surgeon.

Area of Science:

• Spine Surgery
• Medical Technology
• Data Science

Background:

• Emerging technologies, particularly machine learning (ML), are increasingly used in clinical research.
• ML algorithms excel at interpreting large datasets through experiential learning.
• Spine surgery is a field with expanding technological applications.

Purpose of the Study:

• To provide spine surgeons with a comprehensive guide to machine learning.
• To enhance understanding of ML model design and interpretation in surgical practice.
• To highlight common pitfalls in applying ML to spine surgery data.

Main Methods:

• Review of machine learning applications in spine surgery literature.
• Analysis of common ML model designs used in clinical research.
• Identification of key interpretation strategies and potential pitfalls.

Main Results:

• Machine learning offers powerful tools for analyzing complex spine surgery datasets.
• Understanding model design is crucial for accurate interpretation of ML findings.
• Awareness of common pitfalls can prevent misinterpretation of results.

Conclusions:

• Spine surgeons can benefit from a structured understanding of machine learning.
• Accurate interpretation of ML findings is essential for evidence-based surgical practice.
• This guide aims to improve the application of ML in spine surgery research and practice.