The application of machine learning to balance a total knee arthroplasty.

ABSTRACT:

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.

Methods

Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignmen

SUBMITTER: Verstraete MA 

PROVIDER: S-EPMC7677727 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature