ArticleJournal of applied biomedicine2020
Automatic multi-class intertrochanteric femur fracture detection from CT images based on AO/OTA classification using faster R-CNN-BO method.
Article in Journal of applied biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review.Journal of orthopaedic surgery and research · 2022Pooled it
- Deriving the OTA/AO fracture classification from routinely collected radiology reports using a large language model.OTA international : the open access journal of orthopaedic trauma · 2026Article
- Impact of evidence-based nursing intervention based on Watson's theory on rehabilitation compliance in elderly patients after intertrochanteric femur fracture surgery.Frontiers in medicine · 2026Article
- External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classification.BMC musculoskeletal disorders · 2024Observational
- Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation.Journal of personalized medicine · 2024Article
- Effects of dexmedetomidine-ropivacaine assisted combined spinal-epidural anesthesia on neutrophil-lymphocyte ratio and postoperative delirium in elderly patients with intertrochanteric femoral fracture.Frontiers in pharmacology · 2024Article
- Posterior Protrusion Measures (PPM) for Three-Dimensional (3D) CT Classification of Pertrochanteric Fractures.Cureus · 2023Article
- Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2023Review
- Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth.Scientific reports · 2023Article
- Egg Freshness Prediction Model Using Real-Time Cold Chain Storage Condition Based on Transfer Learning.Foods (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma.Computational intelligence and neuroscience · 2022Article
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Authors and funding
4 authors.
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Abstract
Intertrochanteric (IT) femur fractures are the most common fractures in elderly people, and they lead to significant morbidity, mortality, and reduced quality of life. The different types of fractures require a careful definition to ensure accurate surgical planning and reduce the operation time, healing time, and number of surgical failures. In this study, a deep learning-based automatic multi-class IT fracture detection model was developed using computed tomography (CT) images and based on the AO/OTA classification method. The original CT image was resized and rearranged according to the fracture location and an unsharp masking filter was applied. A multi-class classification of nine different types of IT fractures and no fracture was performed using the faster regional-convolutional neural network (R-CNN). Bayesian optimization was also implemented to determine the optimal hyperparameter values for the faster R-CNN algorithm. In our proposed model, IT fractures classified into two classes showed an average accuracy of 0.97 ± 0.02, which was 0.90 ± 0.02 when classified into ten classes. Additionally, the detected region of interest from our proposed model showed minimum root mean square error and intersection over union values of 16.34 ± 47.01 pixels and 0.87 ± 0.12, respectively. In the future, our proposed automatic multi-class IT femur fracture detection model could allow clinicians to identify the fracture region and diagnose different types of femur fractures faster and more accurately. This will increase the probability of correct surgical treatment and minimize postoperative complications.
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