ArticleRadiology2016
Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT.
Article in Radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.Biomedical engineering online · 2024Pooled it
- Function of low back muscle exercise : Preventive effect of refracture analysis of postoperative vertebral fractures.Der Orthopade · 2019Trial
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Integrating AI Segmentation, Simulated Digital Twins, and Extended Reality into Medical Education: A Narrative Technical Review and Proof-of-Concept Case Study.Journal of personalized medicine · 2026Review
- Classification of rib fracture types from postmortem computed tomography images using deep learning.Forensic science, medicine, and pathology · 2024Article
- VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays.Scientific reports · 2024Article
- Weakly-Supervised Detection of Bone Lesions in CT.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Article
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 2024Review
- Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning.International journal of computer assisted radiology and surgery · 2023Article
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
- Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography.Frontiers in endocrinology · 2023Article
- Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.JAMA network open · 2022Article
- Artificial intelligence for radiological paediatric fracture assessment: a systematic review.Insights into imaging · 2022Article
- RiFNet: Automated rib fracture detection in postmortem computed tomography.Forensic science, medicine, and pathology · 2022Article
- Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.Brain & spine · 2022Review
- Efficacy of a novel percutaneous pedicle screw fixation and vertebral reconstruction versus the traditional open pedicle screw fixation in the treatment of single-level thoracolumbar fracture without neurologic deficit.Frontiers in surgery · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT). MATERIALS AND
methodsThis retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed.
resultsTraining set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]).
conclusionThe fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a high sensitivity and a low false-positive rate.
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Registered trials
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.