ArticleDiagnostics (Basel, Switzerland)2021
Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines 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
- Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled 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
- The Impact of Radiomics Image Analysis on Adult Hip Pathologies: A Scoping Review.Journal of clinical medicine · 2026Review
- Multi-Spectral MRI-Based Radiomic Features Can Classify Femoral Loosening in Patients With Total Hip Arthroplasty.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- Multi-Radiomics Features of Super-Resolution MRI for Predicting Minor Amputation in Wagner 3 Diabetic Foot Ulcers.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- CT-based machine learning radiomics modeling to screen for lumbar spine osteoporosis.Frontiers in medicine · 2026Article
- Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.BMC medical imaging · 2025Article
- Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.Medical & biological engineering & computing · 2025Article
- Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.Frontiers in immunology · 2025Article
- Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.BMC medical informatics and decision making · 2024Article
- Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.BMC musculoskeletal disorders · 2024Article
- Article
- Deep learning enables the differentiation between early and late stages of hip avascular necrosis.European radiology · 2024Article
- Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.BJR artificial intelligence · 2024Article
- A single sequence MRI-based deep learning radiomics model in the diagnosis of early osteonecrosis of femoral head.Frontiers in bioengineering and biotechnology · 2024Article
- A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia.Scientific reports · 2023Article
- Radiomics for the Detection of Active Sacroiliitis Using MR Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics method in the differential diagnosis of diabetic foot osteomyelitis and charcot neuroarthropathy.The British journal of radiology · 2023Article
- Machine Learning IntegratingCancers · 2023Article
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Authors and funding
11 authors.
Funding
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Abstract
Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost (
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