Diagnostic Imaging of Spinal Deformities
Bibliographic record
Abstract
STUDY DESIGN: Clinical trial comparing image quality and entrance dose between Biospace EOS system, a new slot-scanning radiographic device, and a Fuji FCR 7501S computed radiography (CR) system for 50 patients followed for spinal deformities. OBJECTIVE: Based on their physical properties, slot-scanners show the potential to produce image quality comparable to CR systems using less radiation. This article validates this assertion by comparing a new slot-scanner to a CR system through a wide-ranging evaluation of dose and image quality for scoliosis examinations. SUMMARY OF BACKGROUND DATA: For each patient included in this study, lateral and posteroanterior images were acquired with both systems. For each system, entrance dose was measured for different anatomic locations. METHODS: Dose and image quality being directly related, comparable images were obtained using the same radiograph tube voltage on both systems while tube currents were selected to match signal-to-noise ratios on a phantom. Different techniques were defined with respect to patient's thickness about the iliac crests. Given dose amplitudes expected for scoliosis examinations, optically stimulated luminescence dosimeters were chosen as optimal sensors. Two radiologists and 2 orthopedists evaluated the images in a randomized order using a questionnaire targeting anatomic landmarks. Visibility of the structures was rated on a 4 level scale. Image quality assessment was analyzed using a Wilcoxon signed-rank tests. RESULTS: Average skin dose was reduced from 6 to 9 times in the thoracoabdominal region when using the slot-scanner instead of CR. Moreover, image quality was significantly better with EOS for all structures in the frontal view (P < 0.006) and lateral view (P < 0.04), except for lumbar spinous processes, better seen on the CR (P < 0.003). CONCLUSION: We established that the EOS system offers overall enhanced image quality while reducing drastically the entrance dose for the patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".