ArticleFrontiers in immunology2025
Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Medical Imaging-Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis.Skeletal radiology · 2026Article
- Deep learning models for osteonecrosis of the femoral head on MRI: a systematic review and meta-analysis of diagnostic performance, staging accuracy, and external generalizability.Skeletal radiology · 2026Review
- Artificial Intelligence Applications in MRI for the Diagnosis and Management of Osteonecrosis of the Femoral Head: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Global Attention Mechanism-Enhanced Deep Learning for Multi-Sequence MRI Diagnosis of Early Femoral Head Osteonecrosis.Annals of the New York Academy of Sciences · 2026Article
- CT-based radiomics for modeling surgical decision-making in young and middle-aged patients with ARCO stage III osteonecrosis of the femoral head: an age-stratified retrospective study.BMC musculoskeletal disorders · 2026Article
- An explainable prognostic model after vascularized bone grafting for hip preservation based on CT radiomics combined with SHAP.Physical and engineering sciences in medicine · 2026Article
- The Impact of Radiomics Image Analysis on Adult Hip Pathologies: A Scoping Review.Journal of clinical medicine · 2026Review
- Racial disparities in pain and total knee arthroplasty across knee osteoarthritis phenotypes.Frontiers in aging · 2026Article
- Sequence-specific radiomics for diagnosis of spinal bone loss.Frontiers in endocrinology · 2026Article
- Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model.Osteoarthritis and cartilage · 2025Article
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based machine learning models using MRI to differentiate between those two disorders and compare their efficacies to those of medical experts. Methods: 140 MRI scans were retrospectively collected from the electronic medical records. They were split into training and testing sets in a 7:3 ratio. Handcrafted radiomics features were harvested following the careful manual segmentation of the regions of interest (ROI). After thoroughly selecting these features, various machine learning models have been constructed. The evaluation was carried out using receiver operating characteristic (ROC) curves. Then NaiveBayes (NB) was selected to establish our final Radiomics-model as it performed the best. Three users with different expertise and backgrounds diagnosed and labeled the dataset into either OA or ONFH. Their results have been compared to our Radiomics-model. Results: The amount of handcrafted radiomics features was 1197 before processing; after the final selection, only 12 key features were retained and used. User 1 had an AUC of 0.632 (95% CI 0.4801-0.7843), User 2 recorded an AUC of 0.565 (95% CI 0.4102-0.7196); while User 3 was on top with an AUC of 0.880 (95% CI 0.7753-0.9843). On the other hand, the Radiomics model attained an AUC of 0.971 (95% CI 0.9298-1.0000); showing greater efficacy than all other users. It also demonstrated a sensitivity of 0.937 and a specificity of 0.885. DCA (Decision Curve Analysis displayed that the radiomics-model had a greater clinical benefit in differentiating OA and ONFH. Conclusion: We have successfully constructed and evaluated an interpretable radiomics-based machine learning model that could distinguish between OA and ONFH. This method has the ability to aid both junior and senior medical professionals to precisely diagnose and take prompt treatment measures.
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