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Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach
Mattia Loppini1,2,3, Francesco Manlio Gambaro1, Katia Chiappetta2
1Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy.
Bioengineering (Basel, Switzerland)
|July 25, 2022
Summary
This study introduces an automated deep learning system for detecting hip prosthesis failure using X-rays. The advanced system accurately identifies loosening, improving total hip arthroplasty follow-up.
Area of Science:
- Orthopedic Surgery
- Artificial Intelligence in Medicine
- Medical Imaging Analysis
Background:
- Conventional total hip arthroplasty (THA) follow-up relies on serial X-rays for early detection of implant failure.
- Prosthetic loosening is a common complication requiring timely identification to prevent adverse outcomes.
- There is a need for automated systems to enhance the efficiency and accuracy of radiographic assessments in THA.
Purpose of the Study:
- To develop and evaluate an automated radiographic failure detection system for total hip arthroplasty.
- To leverage deep learning for precise identification of prosthetic loosening from standard X-ray images.
- To improve the early detection of implant failure in THA patients.
Main Methods:
- Utilized a dataset of 630 patients undergoing THA, with a focus on cases requiring revision for prosthetic loosening.
- Employed a convolutional neural network (DenseNet169) for image analysis, incorporating transfer learning and fine-tuning.
- Standardized radiographic views (anteroposterior and lateral) were pre-processed before analysis by the deep learning model.
Main Results:
- The automated system achieved high performance metrics on the test set: 0.97 classification accuracy, 0.97 sensitivity, and 0.97 specificity.
- The Receiver Operating Characteristic Area Under the Curve (ROC AUC) reached 0.99, indicating excellent discrimination.
- Out of 630 patients, only five images were misclassified, demonstrating the system's high precision in detecting prosthesis failure.
Conclusions:
- The proposed deep learning approach demonstrates exceptional precision in detecting hip prosthesis loosening.
- Automated radiographic analysis offers a highly accurate and efficient method for THA follow-up.
- This technology has the potential to significantly enhance the early identification of implant failure in total hip arthroplasty.
