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Record W2094196195 · doi:10.4236/oja.2012.23011

Ultrasound Imaging of Spinal Vertebrae to Study Scoliosis

2012· article· en· W2094196195 on OpenAlexaff
Wei Chen, Lawrence H. Le, Edmond Lou

Bibliographic record

VenueOpen Journal of Acoustics · 2012
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScoliosisMedicineVertebraCobb angleCurvatureDeformityOrthodonticsLaminaAnatomyUltrasoundCadaverRadiologySurgeryMathematicsGeometry

Abstract

fetched live from OpenAlex

Scoliosis is a musculoskeletal disorder manifested as a three-dimensional spinal deformity. It affects 2% - 3% of the adolescent population. The conventional method to diagnose scoliosis is to measure the Cobb angle from posteroante-rior radiograph. Since radiation exposure is not desirable for patients, other non-ionizing methods have been explored. Among all the non-ionization methods, ultrasound (US) is a potential cost-effective method. However, our understand-ing of US interaction with the vertebrae or the spine is limited. The purpose of this study was to demonstrate the ability of US to identify bony landmarks for measuring spinal deformity. This study used a phased array US system with a 5 MHz transducer and a position encoder. In-vitro experiment on a cadaver vertebra and a pilot clinical study were carried out. The in-vitro experimental results showed that the lamina and spinous process were strong reflectors from the single vertebra. Less than 4% of error occurred on the dimension measurements. The pilot study was performed on a healthy subject and a scoliotic patient. The results indicated the lamina and spinous process could be identified and the curvature of the spine could be estimated using the reflectors. The difference of the curvature angle of the spine measured from the radiograph and the US images was 2?. These results have illustrated that US is a promising tool to measure curvature of spinal deformity and study scoliosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.371
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2012
Admission routes1
Has abstractyes

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