Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review

Area of Science:

• Orthopedic Surgery
• Medical Informatics
• Data Science

Background:

• Total hip arthroplasty (THA) outcomes are crucial for patient recovery.
• Predicting functional outcomes aids in surgical planning and patient management.
• Machine learning (ML) offers advanced analytical capabilities for complex medical data.

Purpose of the Study:

• To systematically review the reliability of ML techniques in predicting functional outcomes after THA.
• To compare the performance of ML models against traditional regression analyses.

Main Methods:

• A comprehensive literature search was conducted up to October 2023 across major databases (MEDLINE/PubMed, Embase, Web of Science, NIH Clinical Trials).
• Included studies encompassed Level I to IV evidence, involving 44,121 patients.
• Seven studies utilizing one to six ML techniques were analyzed, with follow-up periods ranging from 3 months to over 2 years.

Main Results:

• ML models demonstrated high reliability in predicting health-related quality of life (HRQoL) outcomes, achieving an area under the curve (AUC) >84%.
• Prediction of hip-specific functional outcomes showed slightly lower reliability (AUC 71%-87%).
• Random forest and neural networks were generally the top-performing ML models. ML showed a potential advantage over traditional regression for hip-specific function prediction.

Conclusions:

• ML techniques provide acceptable to excellent discrimination for predicting functional outcomes after THA.
• ML may offer a marginal advantage over traditional regression analysis, particularly for predicting hip-specific functional outcomes.