Automatic detection of spinal injuries under dynamic compressive loading using high-speed cine-radiography
Bibliographic record
Abstract
Every year, new cases of individuals suffering from traumatic spinal injuries are detected. Advances in numerical models have allowed for the understanding of the damage caused by trauma and its impact on the patient's life. However, the kinematics and dynamics of vertebral fracture formation from its point of origin to the speed of propulsion of the fragments remain unknown. This is mainly due to the lack of data that essentially includes high-speed videos, load and displacement measurements during experimental tests reproducing spinal traumatic loading conditions. This lack of data can be addressed by the analysis of X-Ray images of animal specimens acquired during the traumatic spinal injury formation process. Thus, the purpose of this study was to develop an approach to automatically detect and track in vitro vertebral fractures using high-speed cine-radiography imaging. Four segments of porcine thoracolumbar vertebrae were dynamically compressed using a servo-hydraulic test bench. The compression process was filmed with a custom high-speed cine-radiography device, and the imaging parameters were optimized based on the physical properties of vertebrae. This paper demonstrates the feasibility of using high-speed cine-radiography imaging in this way, combined with an image processing pipeline to allow automatic documentation of the fracture's appearance and its evolution in the vertebra over time.Clinical Relevance- The proposed method will provide helpful information for proper handling of traumatic spinal injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".