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Related Experiment Video

Updated: Jul 29, 2025

Imaging of the Microstructural Failure Mechanism in the Human Hip
08:43

Imaging of the Microstructural Failure Mechanism in the Human Hip

Published on: September 29, 2023

873

Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development,

Federico Muscato1, Anna Corti2, Francesco Manlio Gambaro3

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy.

International Journal of Medical Informatics
|May 23, 2023
PubMed
Summary

This study developed a deep learning and machine learning approach to automatically detect hip prosthetic failure from radiographs. The combined model achieved high accuracy, showing potential for improved detection of hip implant failure.

Keywords:
Artificial IntelligenceFeature extractionHip ReplacementImage classificationPCA algorithm

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Area of Science:

  • Orthopedic surgery
  • Medical imaging
  • Artificial intelligence in healthcare

Background:

  • Revision hip arthroplasty outcomes are less favorable than primary procedures.
  • Understanding the timing of total hip arthroplasty failure is crucial.
  • Automated detection of prosthetic failure can aid clinical decision-making.

Purpose of the Study:

  • To develop a combined deep learning (DL) and machine learning (ML) approach for automatic detection of hip prosthetic failure.
  • To utilize conventional plain radiographs for prosthesis failure detection.

Main Methods:

  • Two patient cohorts (280 for development, 352 for validation) were analyzed.
  • Radiographs (anteroposterior and lateral views) were pre-processed into original, acetabulum, and stem images.
  • Convolutional neural networks extracted deep features, analyzed via SVM and RF classifiers within original and 3-image pipelines.

Main Results:

  • The Support Vector Machine (SVM) applied to the 3-image pipeline achieved the highest performance.
  • Internal validation accuracy was 0.958 ± 0.006, with an external validation F1-score of 0.874.
  • Explainability analysis confirmed the importance of original, acetabulum, and stem image features.

Conclusions:

  • The developed DL-ML procedure effectively detects hip prosthetic failure using plain radiographs.
  • This approach shows significant potential for improving the diagnosis of hip implant failure.