Learning curve in image-based robotic assisted total knee arthroplasty: a MAKO-robot experience

Area of Science:

• Orthopedic Surgery
• Robotics in Medicine
• Surgical Workflow Analysis

Background:

• Robotic-assisted total knee arthroplasty (RA-TKA) is gaining traction for enhancing precision in implant positioning and soft-tissue balance.
• Understanding the learning curve for specific components of RA-TKA is crucial for efficient adoption and training.

Purpose of the Study:

• To characterize the learning curve associated with MAKO-assisted total knee arthroplasty (RA-TKA).
• To evaluate the independent contributions of different operational components to the learning curve.
• To assess the differential impact of these components on operative workflow.

Main Methods:

• A retrospective observational study of 92 consecutive patients undergoing image-based RA-TKA (MAKO).
• Procedures performed by a single surgeon new to robotic-assisted surgery.
• Cumulative sum (CUSUM) analysis with piecewise linear regression applied to surgical time, pin placement, and robotic workflow time.

Main Results:

• The learning curve for total surgical time, pin placement, and robotic workflow concluded by case 11.
• Optimized performance for total surgical time and pin placement was achieved by case 52.
• Robotic workflow time demonstrated a more gradual improvement, stabilizing later in the series.

Conclusions:

• MAKO-assisted TKA exhibits a structured learning curve with early proficiency (11 cases).
• Optimized performance for surgical time and pin placement occurs mid-series, while robotic workflow requires longer consolidation.
• Findings support RA-TKA's safety, efficacy, and potential for rapid surgeon adaptation.