Validation of a novel handheld 3D ultrasound system for imaging scoliosis – phantom study
Bibliographic record
Abstract
Use of 3D ultrasound (US) scanners to detect and monitor scoliosis have been validated. The Cobb angle, axial vertebral rotation, spinal flexibility, curvatures in the sagittal profile and the Cobb angle on the plane of maximum curvature (PMC) can be measured from coronal, transverse and sagittal planes of ultrasound images. However, traditional 3D ultrasound scanners are relatively bulky and expensive. 2D US handheld and low-cost scanners are widely available. To adapt the 2D scanners for scoliosis applications, a position and orientation system is integrated with the scanner. The objective of this study was to validate a newly developed 3D handheld US system to image the spine. The wireless handheld US scanner (C3-HD, Clarius, Canada) was selected because of its high resolution and availability of raw data. A wireless tracking system based on electromagnetic (G4 system, Polhemus, USA) was integrated with the Clarius ultrasound. During scanning, the ultrasound information was synchronized with the scanner's position and orientation by using custom developed software. Both information were streamed wirelessly to a laptop. Custom software reconstructed and displayed the 3D spinal image in real-time. A single 3D printed vertebra, two full plastic spine phantoms from T1-T12 vertebrae and a non-scoliotic volunteer were scanned. The 3D reconstruction process of a spine image was less than 3 seconds. The dimensional and the angle errors were 1 mm and 3°, respectively. This study demonstrated that a low-cost ($11,000 USD) handheld 3D ultrasound system was developed and validated. Clinical trials on subjects attending will be the next step.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".