SCOLIOSIS ASSESSMENT VIA TELEHEALTH USING PHOTOGRAMMETRY: DEVELOPMENT AND ACCURACY
Bibliographic record
Abstract
ABSTRACT The aim of this study was to present the development and assessment of the measurement accuracy of variables obtained with the Digital Imaged-based Postural Assessment (DIPA©) Capture app and Analysis software as part of a telehealth assessment protocol for idiopathic scoliosis. To enable the correct capture of photographic images for further analysis, the development of an application, called DIPA© Capture, was performed. A photo is automatically obtained only when the smartphone is aligned. The sample of this prospective study was composed of consecutive images of a real plumb line (RPL) taken by using the DIPA© Capture app. Once the photo was captured the app automatically drew a virtual plumb line (VPL). The inclination of the RPL and VPL was measured using the DIPA-S© eHealth Analysis software. A total of 50 images using the App comprised this sample. The median (min-max) inclination angle of the real and virtual plumb lines was 89.8° (88.6°-90°) with a 1.4° range and 90° (89.4°-90°) with a 0.6° range, respectively. The mean difference between the inclination of the two plumb lines (RPL - VPL) was very small -0.1°±0.04° (p= 0.017). The RMS error was 0.3°. The DIPA© Capture app and Analysis software for image acquisition and measurement was developed and is ready for testing with patients. The app accurately captures the alignment of the smartphone during the image acquisition and the software shows adequate measurement accuracy for future assessment of idiopathic scoliosis by photogrammetry. Level of Evidence III; Diagnostic Studies - Investigation of a Diagnostic Test.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".