Machine Learning-Based Automatic Implant Size Prediction From CT Images in Total Knee Arthroplasty

Abstract

Prediction of suitable implant sizes from CT images of a joint anatomy can be achieved using templating methods. Automatic templating (that requires no manual intervention) is useful for speeding up computer or robot-assisted surgical plan generation. In our previous work, the automatic templating task in total knee arthroplasty is achieved by automatic bone segmentation followed by matching a set of anatomical landmarks with the corresponding points on the candidate implants (of different sizes). This paper improves the approach by adding a linear regression module to the framework and thereby increases the prediction accuracy significantly without increasing the processing time. Experimental analysis on 292 knee CT images proved that the proposed approach predicts the implant sizes in more than 95% of femur cases and in more than 98% of tibia cases with at most 1-size difference from the sizes used by an experienced surgeon.