Machine Learning in Orthopedics: A Literature Review

Area of Science:

• Orthopedic research
• Biomedical engineering
• Data science in medicine

Background:

• Machine learning (ML) offers advanced analytical capabilities.
• Orthopedics presents complex challenges suitable for ML solutions.
• A comprehensive overview of ML in orthopedics is needed.

Purpose of the Study:

• To systematically review machine learning applications in orthopedics.
• To identify common ML techniques and their orthopedic uses.
• To assess the data sources and predictive performance reported in the literature.

Main Methods:

• Systematic literature review of articles published in the last two decades.
• Searches conducted in Scopus and Medline databases.
• Analysis of 70 selected journal articles using Grounded Theory.

Main Results:

• Identified prevalent machine learning techniques applied to orthopedic problems.
• Cataloged diverse orthopedic application domains utilizing ML.
• Detailed the types of data used and evaluated the reported predictive performance.

Conclusions:

• Machine learning is increasingly applied across various orthopedic domains.
• Understanding current trends, data, and performance is crucial for future research.
• This review provides a foundation for advancing ML in orthopedic care.