ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Deep learning in the diagnosis, staging, and prognosis of osteonecrosis of the femoral head: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Performance of artificial intelligence in automated measurement of patellofemoral joint parameters: a systematic review.Journal of orthopaedic surgery and research · 2025Pooled 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
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease.BMC musculoskeletal disorders · 2025Article
- Enhanced glioma semantic segmentation using U-net and pre-trained backbone U-net architectures.Scientific reports · 2025Article
- Idiopathic Transient Osteoporosis of the Hip: A Frequently Overlooked and Misdiagnosed Cause of Hip Pain.Cureus · 2024Article
- 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 enables the differentiation between early and late stages of hip avascular necrosis.European radiology · 2024Article
- A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and a convolutional neural network (CNN) ensemble, for the accurate differentiation between the two diseases. An augmented dataset of 210 hips with TOH and 210 hips with AVN was used to finetune three ImageNet-trained CNNs (VGG-16, InceptionResNetV2, and InceptionV3). An ensemble decision was reached in a hard-voting manner by selecting the outcome voted by at least two of the CNNs. Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). Precision for the diagnosis of AVN and recall for the detection of TOH were higher in the model ensemble compared to Inception-ResNet-V2. Ensemble performance was significantly higher than that of an MSK radiologist and a fellow (P < 0.001). Deep learning was highly successful in distinguishing TOH from AVN, with a potential to aid treatment decisions and lead to the avoidance of unnecessary surgery.
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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.