ArticleAJNR. American journal of neuroradiology2021
CT Cervical Spine Fracture Detection Using a Convolutional Neural Network.
Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 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
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence for identification of cervical spine fractures: a systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.Emergency radiology · 2026Pooled it
- IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using 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
- A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification 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
- 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
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Potential change in healthcare costs of implementing artificial intelligence for detecting cervical spine fractures on CT: an early health technology assessment.European radiology · 2026Article
- Spinal Cord Segmentation and Injury Detection based on Siamese Conventional WideRes Network using CT Image.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
- Multicenter, Multinational, and Multivendor Validation of an Artificial Intelligence Application for Acute Cervical Spine Fracture Detection on CT.Diagnostics (Basel, Switzerland) · 2026Article
- Cervical Collar in Adult Trauma: Evidence, Controversies, and Clinical Decision-Making.Orthopedic research and reviews · 2026Review
- Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.AJNR. American journal of neuroradiology · 2025Article
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- External Validation of a Winning Artificial Intelligence Algorithm from the RSNA 2022 Cervical Spine Fracture Detection Challenge.AJNR. American journal of neuroradiology · 2025Article
- An efficient deep learning based approach for automated identification of cervical vertebrae fracture as a clinical support aid.Scientific reports · 2025Article
- Development and assessment of the AE-RADS standardized grid for specifically evaluating adverse events in diagnostic radiology and teleradiology.BMC medical imaging · 2025Article
- Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.Radiology. Artificial intelligence · 2024Article
- An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.Journal of imaging informatics in medicine · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.Neurospine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeMultidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT. MATERIALS AND
methodsWe evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cervical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective visualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The ĸ coefficients, sensitivity, specificity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accuracy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth.
resultsConvolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%-94%), with 76% (95% CI, 68%-83%) sensitivity and 97% (95% CI, 95%-98%) specificity. The radiologist accuracy was 95% (95% CI, 94%-97%), with 93% (95% CI, 88%-97%) sensitivity and 96% (95% CI, 94%-98%) specificity. Fractures missed by the convolutional neural network and by radiologists were similar by level and location and included fractured anterior osteophytes, transverse processes, and spinous processes, as well as lower cervical spine fractures that are often obscured by CT beam attenuation.
conclusionsThe convolutional neural network holds promise at both worklist prioritization and assisting radiologists in cervical spine fracture detection on CT. Understanding the strengths and weaknesses of the convolutional neural network is essential before its successful incorporation into clinical practice. Further refinements in sensitivity will improve convolutional neural network diagnostic utility.
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