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Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data.

Thomas A Burge1, Jonathan R T Jeffers2, Connor W Myant3

  • 1Dyson School of Design Engineering, Imperial College, London, SW7 2BU, UK. t.burge20@imperial.ac.uk.

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Summary

This study presents an automated pipeline for custom total knee replacement (TKR) implant design using CT scans. The AI-driven system accurately generates patient-specific implants, offering time and cost savings.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Total knee replacement (TKR) is a common orthopedic procedure.
  • Current implant design methods can be time-consuming and costly.
  • Patient-specific implants may offer improved outcomes.

Purpose of the Study:

  • To develop an automated pipeline for custom total knee replacement implant design.
  • To leverage machine learning and computer-aided design for personalized implants.
  • To assess the accuracy and feasibility of the automated CT-based design process.

Main Methods:

  • Utilized machine learning (classification, object detection, segmentation) on CT scans (DICOM files).
  • Employed statistical shape models to predict femur and tibia 3D surface models.
  • Integrated computer-aided design (CAD) scripts for automated implant generation.
  • Trained and validated the pipeline using data from 98 Korean Asian subjects.

Main Results:

  • The pipeline demonstrated repeatable and highly accurate custom implant designs.
  • Computational fitting showed excellent congruence with ground truth bone models.
  • Performance was consistent across variations in subject sex, age, height, and knee side.

Conclusions:

  • An automated, CT-based pipeline for custom TKR implant design is feasible.
  • The system offers significant time and cost advantages over conventional methods.
  • The framework is adaptable for customising other medical implants.