ArticleFrontiers in medicine2024
Diagnosis and detection of bone fracture in radiographic images using deep learning approaches.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Identifying the diagnostic utility of artificial intelligence for elbow effusion detection: A systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools.Journal of orthopaedic surgery and research · 2026Pooled it
- Deep learning for radiographic differentiation between lateral malleolar avulsion fractures and subfibular ossicles.iScience · 2026Article
- A study on physicians' perceptions of privacy in the context of the e-Nabiz (e-Pulse) in the Turkish healthcare system.Scientific reports · 2026Article
- An Interpretable Hybrid SFNet Deep Learning Framework for Multi-Site Bone Fracture Detection in Medical Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- A transfer learning-based approach for automated bone fracture classification in X-ray imaging.Therapeutic advances in musculoskeletal disease · 2026Article
- Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.Frontiers in artificial intelligence · 2026Review
- Impact of quantization on various CNN architectures for bone fracture detection.Frontiers in medical technology · 2026Article
- AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.Journal of wrist surgery · 2025Article
- A Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- FracFusionNet: A Multi-Level Feature Fusion Convolutional Network for Bone Fracture Detection in Radiographic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Deep learning-based image classification for AI-assisted integration of pathology and radiology in medical imaging.Frontiers in medicine · 2025Article
- Automated Detection of Bone Fractures in Muscle X-Ray Images Using Multiband-Frequency Aware Deep Representation Learning.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection. Methods: Nevertheless, it is essential to develop an automated approach for identifying fractures using the multi-region X-ray dataset from Kaggle, which contains a comprehensive collection of 10,580 radiographic images. This study advocates for the use of DL techniques, including VGG16, ResNet152V2, and DenseNet201, for the detection and diagnosis of bone fractures. Results: The experimental findings demonstrate that the proposed approach accurately identifies and classifies various types of fractures. Our system, incorporating DenseNet201 and VGG16, achieved an accuracy rate of 97% during the validation phase. By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture detection and diagnosis and proposes a system that improves accuracy. Conclusion: The findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.
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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.