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Record W3173064324 · doi:10.3233/bmr-200259

Musculoskeletal ultrasound for 3D bone modeling: A preliminary study applied to lumbar vertebra

2021· article· en· W3173064324 on OpenAlexaff
Andrew Forbes, Vincent Cantin, Yann Develle, Yves Dubé, Antony Bertrand‐Grenier, C. Ménard-Lebel, Stéphane Sobczak

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2021
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-QuébecUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsUltrasoundMedicineLumbar vertebraeLumbarRadiologyVertebraFluoroscopyTomographyBiomedical engineeringNuclear medicineAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: There is no non-invasive in vivo method to assess intervertebral kinematics. Current kinematics models are based on in vitro bone reconstructions from computed tomography (CT)-scan imaging, fluoroscopy and MRIs, which are either expensive or deleterious for human tissues. Musculoskeletal ultrasound is an accessible, easy to use and cost-effective device that allows high-resolution, real-time imaging of bone structure. OBJECTIVE: The aim of this preliminary study was to compare the concordance of 3D bone modeling of lumbar vertebrae between CT-scan and ultrasound imaging and to study the intra and inter-reliability of distances measured on 3D ultrasound bone models. METHODS: CT-scan, ultrasound, and in situ data of five lumbar vertebrae from the same human specimen were used. All vertebrae were scanned by tomography and a new musculoskeletal ultrasound procedure. Then, 3D bone modeling was created from both CT-scan and ultrasound image data set. Distances between anatomical bones landmarks were measured on the 3D models and compared to in situ measurements.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.259
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations4
Published2021
Admission routes1
Has abstractyes

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