ArticleDiagnostics (Basel, Switzerland)2023
Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence for identification of cervical spine fractures: a systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- ShuffleLeNet architecture for spinal cord injury classification and level detection.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.Scientific reports · 2026Article
- Multi-class cervical spine fracture classification using deep ensemble model based on CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- An explainable ResNet50-BiLSTM-attention framework with spatial token modeling and imbalance-aware learning for multi-class knee osteoarthritis severity grading.Frontiers in medicine · 2026Article
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.Journal of imaging informatics in medicine · 2025Article
- Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice.Korean journal of neurotrauma · 2025Review
- Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection.Biomolecules & biomedicine · 2025Article
- Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia.Journal of imaging informatics in medicine · 2025Article
- Interpretable Multi-Label Classification for Tibiofibula Fracture 2D CT Images with Selective Attention and Data Augmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Application and Prospects of Deep Learning Technology in Fracture Diagnosis.Current medical science · 2024Review
- Detection and Localization of Spine Disorders from Plain Radiography.Journal of imaging informatics in medicine · 2024Article
- Radiographic and clinical evaluation of external pedicle screw fixation as a definitive solution for selective acetabular fractures: a retrospective analysis.BMC musculoskeletal disorders · 2024Article
- Watch Your Back! How Deep Learning Is Cracking the Real World of CT for Cervical Spine Fractures.Radiology. Artificial intelligence · 2024Article
- A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.Scientific reports · 2024Article
- Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM.Journal of imaging informatics in medicine · 2024Article
- Explainable artificial intelligence in orthopedic surgery.Journal of experimental orthopaedics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.
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