Related Experiment Video
Updated: Apr 18, 2026
09:51
The Transition to an Anterior-Based Muscle Sparing Approach Improves Early Postoperative Function but is Associated with a Learning Curve
Published on: September 7, 2022
3.9K
Platform-Specific Learning Curves in Robotic-Assisted Total Knee Arthroplasty: A Systematic Review.
Ryhan Divyang Patel1, Praneshraja Ganesaraja1, Kapil Sugand1
1Imperial College London, London, UK.
Orthopaedic Surgery
|April 16, 2026
Summary
The learning curve for robotic-assisted total knee arthroplasty varies by system. Proficiency is achieved after a mean of 18-34 cases, depending on the robotic platform used.
Area of Science:
- Orthopedic Surgery
- Robotics in Medicine
- Surgical Education
Background:
- Robotic-assisted total knee arthroplasty (raTKA) offers potential for improved surgical precision and implant alignment.
- Characterizing the learning curve (LC) for raTKA is crucial for effective training and safe clinical implementation.
- Existing data on raTKA learning curves across different robotic platforms is limited and poorly understood.
Purpose of the Study:
- To systematically review and quantify the learning curve (LC) for robotic-assisted total knee arthroplasty (raTKA).
- To determine the number of cases required for procedural proficiency across major raTKA systems.
- To analyze operative time, radiographic alignment, complication rates, and patient-reported outcome measures (PROMs) related to the raTKA learning curve.
Main Methods:
- A systematic literature search was conducted across major databases (MEDLINE, Embase, Scopus, Web of Science, CENTRAL) following PRISMA 2020 guidelines.
- Included studies reported original data on learning curves in raTKA, focusing on operative time and proficiency.
- Forty studies involving 10,533 procedures across nine robotic platforms were analyzed, calculating weighted means for operative time and cases to proficiency.
Main Results:
- The learning curve for raTKA is platform-dependent, with proficiency achieved after a mean of 18.4 cases (NA VIO), 29.5 cases (ROSA), and 34.2 cases (MAKO).
- Operative time decreased with experience, stabilizing around 81-85 minutes for these platforms.
- Radiographic accuracy and complication rates remained consistent throughout the learning curve; PROMs were inconsistently reported.
Conclusions:
- Robotic-assisted total knee arthroplasty learning curves are significantly influenced by the specific robotic system utilized.
- Standardized definitions for learning curves and comprehensive outcome reporting are essential for effective training and safe integration of raTKA.
- Further comparative studies are needed to guide surgeon training, accreditation, and clinical adoption of raTKA technologies.
