ArticleBioengineering (Basel, Switzerland)2022
Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- Performance of Artificial Intelligence Models in Radiographic Image Analysis for Predicting Hip and Knee Prosthesis Failure: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- Current Advances of Artificial Intelligence and Machine Learning in Orthopaedics: A Focus on Hip Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- The reliability of deep learning models in assessing the shoulder arthroscopic field's visual clarity in relation to bleeding.JSES international · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty.Computational and structural biotechnology journal · 2025Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Leveraging transfer learning for predicting total knee arthroplasty failure from post-operative radiographs.Journal of experimental orthopaedics · 2024Article
- Can machine learning predict the accuracy of preoperative planning for total hip arthroplasty, basing on patient-related factors? An explorative investigation on Supervised machine learning classification models.Journal of clinical orthopaedics and trauma · 2024Article
- Guideline for RSA and CT-RSA implant migration measurements: an update of standardizations and recommendations.Acta orthopaedica · 2024Article
- AI-based hip prosthesis failure prediction through evolutional radiological indices.Archives of orthopaedic and trauma surgery · 2024Article
- Changes in serum inflammatory factors after hip arthroplasty and analysis of risk factors for prosthesis loosening.American journal of translational research · 2024Article
- Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.Bioengineering (Basel, Switzerland) · 2023Article
- A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were included in the study, two thirds of which needed total or partial revision for prosthetic loosening. The analysis is based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing for proper standardization, images were analyzed through a convolutional neural network (the DenseNet169 network), aiming to predict prosthesis failure. The entire dataset was divided in three subsets: training, validation, and test. These contained transfer learning and fine-tuning algorithms, based on the training dataset, and were implemented to adapt the DenseNet169 network to the specific data and clinical problem. Results: After the training procedures, in the test set, the classification accuracy was 0.97, the sensitivity 0.97, the specificity 0.97, and the ROC AUC was 0.99. Only five images were incorrectly classified. Seventy-four images were classified as failed, and eighty as non-failed with a probability >0.999. Conclusion: The proposed deep learning procedure can detect the loosening of the hip prosthesis with a very high degree of precision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.