Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?

Khashayar Ghadirinejad; Stephen Graves; Richard de Steiger; Nicole Pratt; Lucian B. Solomon; Mark Taylor; Reza Hashemi

doi:10.1016/j.knee.2025.10.020

November 1, 2025The KneeOpen Access

Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to evaluate how machine learning can be integrated into post-market surveillance of knee prostheses.
Identifying prosthesis outliers using machine learning techniques.
Analyzing confounding factors affecting prosthesis performance.
Machine learning shows potential for enhancing the identification of prostheses with performance issues.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Machine learning identifies prosthesis outliers in post-market surveillance, suggesting improvements for early detection.

Abstract

Machine learning may offer a supplementary approach for the identification of prosthesis outliers. However, further analysis is required to fully comprehend the effect of confounding factors and the potential contribution of ML to the early identification of outliers.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Ghadirinejad et al. (2025) studied this question.

synapsesocial.com/papers/6925437fc0ce034ddc358ed4 https://doi.org/10.1016/j.knee.2025.10.020

View Full Paper

Ask AI

Bookmark

Share

Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading