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Related Experiment Video

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Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.

Samira Al-Nasser1, Siamak Noroozi1, Adrian Harvey2

  • 1Bournemouth University, Fern Barrow, Poole BH12 3BB, UK.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary

A novel intraoperative load sensor for total knee replacements (TKRs) shows good accuracy and precision. Software improvements could further enhance its performance in joint surgeries.

Keywords:
artificial intelligenceintraoperative load measuringjoint force sensortotal knee replacement

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Area of Science:

  • Biomedical Engineering
  • Orthopedic Surgery
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Total knee replacements (TKRs) can be improved with intraoperative tibial sensors to enhance patient outcomes and reduce revision surgeries.
  • Accurate, precise, and wide-ranging load tracking is crucial for the success of joint force monitoring in TKRs.

Purpose of the Study:

  • To investigate the accuracy and precision of a novel intraoperative load sensor designed for total knee replacements (TKRs).
  • To assess the sensor's performance across different load conditions and spatial areas within the knee joint.

Main Methods:

  • A self-developed intraoperative load sensor, compatible with Zimmer's Persona Knee System and adaptable to others, was utilized.
  • Artificial intelligence (AI) was employed to analyze sensor data.
  • Accuracy and precision were evaluated by testing five points on medial/lateral sides, inside/outside the sensing area, and across varying load ranges.

Main Results:

  • The sensor achieved an average accuracy of 83.41% for load prediction and 84.63% for location prediction.
  • Highest accuracy (99.20%) was observed within the sensing area and training load range; expanding this range could improve overall accuracy.
  • Load and location predictions showed comparable accuracy and precision across compartments and sensing areas (p > 0.05), but accuracy differed significantly below the training load range (p < 0.05).

Conclusions:

  • The intraoperative load sensor demonstrates good accuracy and precision across its entire surface and a broad load spectrum.
  • Minor software enhancements have the potential to significantly improve sensor performance.
  • A reliable and robust intraoperative load sensor could drive advancements in all joint replacement surgeries.