ArticleJournal of digital imaging2020
Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 6 of them syntheses that pooled it.
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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.
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Who cites it
54 citing papers in PubMed, 6 syntheses or guidelines pooled it, 98 citations in OpenAlex.
- Artificial Intelligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta-Analysis.Orthopaedic surgery · 2025Pooled it
- Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Pooled it
- Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review.Journal of orthopaedic surgery and research · 2022Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- AI-based Hip Fracture Classification from Pelvic X-ray Images Using a Hybrid PEFT-ViT and XGBoost Model.Journal of imaging informatics in medicine · 2026Article
- Evaluating artificial intelligence in the diagnosis of hip fractures: an analysis of sensitivity, specificity, positive and negative predictive values, and accuracy.Skeletal radiology · 2026Article
- Hip joint image quality screening based on the Diffusion Mamba model.Scientific data · 2026Article
- Artificial intelligence-based diagnosis of non-displaced femoral neck fractures shows excellent sensitivity and specificity and reduces the need for computed tomography scans in emergency rooms.Journal of experimental orthopaedics · 2026Article
- Article
- Development and validation of a deep learning-based automatic detection and classification model for femoral neck fractures using hip imaging: a retrospective multicenter diagnostic study.Frontiers in medicine · 2026Article
- Improved YOLOv8 with average pooling downsampling for detection and classification of intertrochanteric femoral fractures in X-ray images: a study focusing on AO/OTA classification.Frontiers in medicine · 2026Article
- Garden classification of femoral neck fracture using deep-learning algorithm.Scientific reports · 2025Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Comparative study on functional outcomes of bipolar hemiarthroplasty versus total hip arthroplasty in elderly patients with displaced femoral neck fractures.Pakistan journal of medical sciences · 2025Article
- Detection and classification of femoral neck fractures from plain pelvic X-rays using deep learning and machine learning methods.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2025Article
- Open-source convolutional neural network to classify distal radial fractures according to the AO/OTA classification on plain radiographs.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Article
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Development and Validation of a Deep Learning System for the Detection of Nondisplaced Femoral Neck Fractures.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Ground truth labels of Garden fracture classification were applied as follows: (1) 127 Garden I and II fracture radiographs, (2) 610 Garden III and IV fracture radiographs, and (3) 326 normal hip radiographs. After localization by an initial network, a second CNN classified the images as Garden I/II fracture, Garden III/IV fracture, or no fracture. Advanced data augmentation techniques expanded the training set: (1) generative adversarial network (GAN); (2) digitally reconstructed radiographs (DRRs) from preoperative hip CT scans. In all, 9063 images, real and generated, were available for training and testing. A deep neural network was designed and tuned based on a 20% validation group. A holdout test dataset consisted of 105 real images, 35 in each class. Two class prediction of fracture versus no fracture (AUC 0.92): accuracy 92.3%, sensitivity 0.91, specificity 0.93, PPV 0.96, NPV 0.86. Three class prediction of Garden I/II, Garden III/IV, or normal (AUC 0.96): accuracy 86.0%, sensitivity 0.79, specificity 0.90, PPV 0.80, NPV 0.90. Without any advanced augmentation, the AUC for two-class prediction was 0.80. With DRR as the only advanced augmentation, AUC was 0.91 and with GAN only AUC was 0.87. GANs and DRRs can be used to improve the accuracy of a tool to diagnose and classify femoral neck fractures.
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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.