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Related Experiment Video

Updated: May 24, 2025

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
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Orthopedic Implant Fixation Type Detection in Hip Arthroplasty Using Two-Staged Low-Shot Network.

Aparna Kanakatte, Divya Bhatia, Rupsha Mukherjee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
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    This study introduces an automated method for identifying hip arthroplasty fixation types from X-rays. The approach achieves high accuracy, aiding in surgical planning for revision procedures.

    Area of Science:

    • Orthopedic surgery
    • Medical imaging analysis
    • Artificial intelligence in healthcare

    Background:

    • Hip arthroplasty implants can fail over time, necessitating revision surgery.
    • The increasing number of primary hip arthroplasties leads to a growing burden of revision surgeries.
    • Accurate identification of implant manufacturer and fixation type is crucial for effective surgical planning and instrument selection.

    Purpose of the Study:

    • To develop an automated method for detecting the fixation type in hip arthroplasty from X-ray images.
    • To address the lack of existing automated solutions for hip arthroplasty fixation type detection.
    • To improve surgical planning and instrument selection for revision hip arthroplasty.

    Main Methods:

    • Annotation of an open-source dataset with hip arthroplasty fixation types (cemented, cementless, hybrid).

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  • Development of a two-stage, low-shot learning approach for fixation type detection.
  • Addressing challenges of inter-class confusion and class imbalance in the dataset.

    • Main Results:

    • Achieved 93% classification accuracy on the annotated dataset.
    • Demonstrated 86% accuracy when blind-tested on an independent clinical dataset.
    • Successfully classified cemented, cementless, and hybrid fixation types.

    Conclusions:

    • The proposed automated method effectively detects hip arthroplasty fixation types from X-ray images.
    • This technology can significantly aid in planning revision surgeries and ensuring the availability of correct instruments.
    • The approach shows promise for clinical application, improving patient outcomes in revision hip arthroplasty.