Systematic Review of Machine Learning for Robotic Knee Replacement Outcomes

A systematic review synthesizing evidence on machine learning applications for predicting outcomes in robotic-assisted total knee arthroplasty patients. The document, authored by Feng Peng and last updated in May 2026, analyzes 47 studies published through January 2025. It reports model performance metrics, such as AUC values between 0.82 and 0.89 for predicting infections and complications.

Use Cases

• Benchmarking machine learning model performance for surgical outcome prediction based on reported AUC ranges
• Identifying research gaps in RA-TKA-specific prediction models based on the finding that only 25.5% of reviewed studies addressed this population
• Evaluating the trade-off between model complexity and clinical utility based on the review's discussion of simple versus complex algorithms

Strengths

• The review is based on 47 included studies, providing a substantial evidence base.
• Specific performance metrics are reported, such as AUC values ranging from 0.82 to 0.89 for predicting periprosthetic joint infection.
• The document includes a critical analysis of tensions in the field, such as the comparable performance of simple three-variable models.

Limitations

• The dataset is a 15.0 KB DOCX file, indicating a very limited scope, likely a summary table or document.
• Row count and column-level documentation are absent; the actual data structure must be inferred after download.
• The geographic scope of the underlying studies is not specified, which may limit generalizability assessments.

Provenance

Source

figshare, author Feng Peng

Collection Method

Systematic literature review from PubMed, Embase, Web of Science, and Cochrane Library.

Time Range

Studies published through January 2025.

Freshness

Last updated 2026-05-20 05:44:15