ArticleScientific reports2023
Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Convolutional neural networks in paediatric fracture detection: pooled evidence from a systematic review and meta-analysis.European radiology · 2026Pooled it
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Automated morphometric segmentation analysis of hand X-ray image using deep learning network.BMC medical imaging · 2026Article
- A Lightweight End-to-End Framework for Real-Time Vehicle-Ejected Debris Detection on Edge Devices.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Multi-Class Pediatric Wrist Fracture Subtype Classification: A Pilot Study Comparing Convolutional Neural Network Architectures.Journal of imaging · 2026Article
- Zero-shot multimodal large language models underperform a domain-trained CNN baseline in pediatric wrist fracture detection.Scientific reports · 2026Article
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Deep learning enables automated detection of dinosaur footprints with high accuracy.Scientific reports · 2026Article
- In silico augmentation strategies for enhanced machine learning performance in fracture recognition.Scientific reports · 2026Article
- A phantom study on the evaluation of the YOLOv8 deep learning model in the detection of tooth ankylosis on cone-beam computed tomography images.Head & face medicine · 2026Article
- An Interpretable Hybrid SFNet Deep Learning Framework for Multi-Site Bone Fracture Detection in Medical Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- A Benchmark X-ray Dataset for Pediatric Supracondylar Humerus Fractures with Improved YOLOv11-Based Detection.Scientific data · 2026Article
- Wavelet Transform and Hierarchical Hybrid Matching for Enhancing End-to-End Pediatric Wrist Fracture Detection.Journal of imaging informatics in medicine · 2026Article
- A Comprehensive X-ray Dataset for Pediatric Ulna and Radius Fractures Analysis.Scientific data · 2026Article
- Article
- Urological diagnostics based on kidney stone detection in CT imaging using YOLOv8 deep learning framework.Frontiers in medicine · 2026Article
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- A lightweight region of interest-level adjudication framework with hard-negative mining and confidence-aware fusion for pediatric fracture detection.Frontiers in artificial intelligence · 2026Article
- MFSD-YOLO: A multi-scale feature detection network for pediatric wrist abnormalities in radiographic images.PloS one · 2026Article
- Deep Learning Based Evaluation of Skeletal Maturation: A Comparative Analysis of Five Hand-Wrist Methods.Orthodontics & craniofacial research · 2025Article
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2 authors.
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Abstract
Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.
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