ArticleArthroplasty (London, England)2022
A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI.
Article in Arthroplasty (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Machine Learning Based Diagnostic Models for Hip and Knee Prosthetic Joint Infection: A Systematic Review.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Pooled it
- Optimizing the management of periprosthetic infections with artificial intelligence: current evidence and future directions.EFORT open reviews · 2026Review
- Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.Bone & joint research · 2026Article
- Global trends in biomarker research for periprosthetic joint infection: a bibliometric analysis.Arthroplasty (London, England) · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Auxiliary diagnosis of periprosthesis joint infection by leukocyte esterase strips test using a deep learning model.International journal of surgery (London, England) · 2025Article
- Artificial Intelligence and Its Role in Predicting Periprosthetic Joint Infections.Biomedicines · 2025Review
- Article
- Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology.NPJ digital medicine · 2024Article
- Digital orthopedics in the new AI era: from ASIA aspect.Arthroplasty (London, England) · 2023Article
- HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text.Sensors (Basel, Switzerland) · 2023Article
- Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review.Arthroplasty (London, England) · 2023Review
- Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThis study aimed to establish a deep learning method based on convolutional networks for the preliminary study of the pathological diagnosis of prosthetic joint infections (PJI).
methodsWe enrolled 20 revision patients after joint replacement from the Department of Orthopedics, the First Medical Center, General Hospital of the People's Liberation Army, from January 2021 to January 2022 (10 of whom were confirmed to be infected against 2018 ICM criteria, and the remaining 10 were verified to be non-infected), and classified high-power field images according to 2018 ICM criteria. Then, we inputted 576 positive images and 576 negative images into a neural network by employing a resNET model, used to select 461 positive images and 461 negative images as training sets, 57 positive images and 31 negative images as internal verification sets, 115 positive images and 115 negative images as external test sets.
resultsThe resNET model classification was used to analyze the pathological sections of PJI patients under high magnification fields. The results of internal validation set showed a positive accuracy of 96.49%, a negative accuracy of 87.09%, an average accuracy of 93.22%, an average recall rate 96.49%, and an F1 of 0.9482. The accuracy of external test results was 97.39% positive, 93.04% negative, the average accuracy of external test set was 93.33%, the average recall rate was 97.39%, with an F1 of 0.9482. The AUC area of the intelligent image-reading diagnosis system was 0.8136.
conclusionsThis study used the convolutional neural network deep learning to identify high-magnification images from pathological sections of soft tissues around joints, against the diagnostic criteria for acute infection, and a high precision and a high recall rate were accomplished. The results of this technique confirmed that better results could be achieved by comparing the new method with the standard strategies in terms of diagnostic accuracy. Continuous upgrading of extended training sets is needed to improve the diagnostic accuracy of the convolutional network deep learning before it is applied to clinical practice.
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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.