ArticleLa Radiologia medica2023
MRI-based artificial intelligence to predict infection following total hip arthroplasty failure.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 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
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- Optimizing arthroplasty outcomes: the impact of artificial intelligence and robotic assistance.Annals of medicine and surgery (2012) · 2026Review
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in Orthopaedic Surgery.Bioengineering (Basel, Switzerland) · 2026Review
- From serum inflammatory markers to fluid, tissue, and molecular assays: current advances in the laboratory diagnosis of bone and joint infections.Frontiers in cellular and infection microbiology · 2026Review
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Artificial Intelligence and Its Role in Predicting Periprosthetic Joint Infections.Biomedicines · 2025Review
- Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.La Radiologia medica · 2025Article
- Imaging in Periprosthetic Joint Infection Diagnosis: A Comprehensive Review.Microorganisms · 2024Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- The role of advanced metal artifact reduction MRI in the diagnosis of periprosthetic joint infection.Skeletal radiology · 2024Review
- US-Guided Interventional Procedures for Total Hip Arthroplasty.Journal of clinical medicine · 2024Review
- Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications.La Radiologia medica · 2024Review
- MRI of total hip arthroplasty: technical aspects and imaging findings.Insights into imaging · 2024Review
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate whether artificial intelligence (AI) can differentiate septic from non-septic total hip arthroplasty (THA) failure based on preoperative MRI features. MATERIALS AND
methodsWe included 173 patients (98 females, age: 67 ± 12 years) subjected to first-time THA revision surgery after preoperative pelvis MRI. We divided the patients into a training/validation/internal testing cohort (n = 117) and a temporally independent external-testing cohort (n = 56). MRI features were used to train, validate and test a machine learning algorithm based on support vector machine (SVM) to predict THA infection on the training-internal validation cohort with a nested fivefold validation approach. Machine learning performance was evaluated on independent data from the external-testing cohort.
resultsMRI features were significantly more frequently observed in THA infection (P < 0.001), except bone destruction, periarticular soft-tissue mass, and fibrous membrane (P > 0.005). Considering all MRI features in the training/validation/internal-testing cohort, SVM classifier reached 92% sensitivity, 62% specificity, 79% PPV, 83% NPV, 82% accuracy, and 81% AUC in predicting THA infection, with bone edema, extracapsular edema, and synovitis having been the best predictors. After being tested on the external-testing cohort, the classifier showed 92% sensitivity, 79% specificity, 89% PPV, 83% NPV, 88% accuracy, and 89% AUC in predicting THA infection. SVM classifier showed 81% sensitivity, 76% specificity, 66% PPV, 88% NPV, 80% accuracy, and 74% AUC in predicting THA infection in the training/validation/internal-testing cohort based on the only presence of periprosthetic bone marrow edema on MRI, while it showed 68% sensitivity, 89% specificity, 93% PPV, 60% NPV, 75% accuracy, and 79% AUC in the external-testing cohort.
conclusionAI using SVM classifier showed promising results in predicting THA infection based on MRI features. This model might support radiologists in identifying THA infection.
Indexed as
Identifiers
What OpenQuestion holds
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.