Performance of Surface Topography Systems in Scoliosis Management: A Narrative Review
Bibliographic record
Abstract
ABSTRACT Introduction The aesthetic appearance of the torso is the foremost concern of scoliotic patients, which, if not addressed, could adversely affect their life quality. The Cobb angle limitation in describing three-dimensional (3D) deformities of the spine and its associated ionizing radiation motivated clinicians to implement noninvasive surface topography (ST) methods for monitoring scoliosis. This study aimed to review the accuracy of the most commonly used ST systems and their ST parameters in predicting and monitoring scoliosis. Materials and Methods The scientific databases were used to search for the studies considering the validity and reliability of different ST methods published in English from 1970 to July 2020. Results Of 221 found publications, 41 journal publications were evaluated for this review. The accuracy of ST methods was affected by light source quality, acquisition time, and postural sway. Some ST parameters resulted in a high correlation with radiographic measurements and classified scoliosis deformities into mild, moderate, and severe. Some ST parameters may not effectively predict the Cobb angle but could monitor curve progression accurately. Representing spine deformities in the lumbar section and subjects with higher body mass index was associated with more significant errors due to thicker soft tissues around the spine. Conclusions According to the present review, ST systems could complement radiography measurements and provide valuable insights into different aspects of internal and external deformity; however, they have not reached a state that can replace radiography in the management of scoliosis. Clinical Relevance The present study helps clinicians choose the most appropriate ST methods for predicting and monitoring scoliotic curves and torso asymmetry evaluations.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".