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Record W2175180303 · doi:10.1364/boe.6.004238

Sensitivity analysis aimed at blood vessels detection using interstitial optical tomography during brain needle biopsy procedures

2015· article· en· W2175180303 on OpenAlexafffund
Julien Pichette, Andréanne Goyette, Fabien Picot, Marie-Andrée Tremblay, Gilles Soulez, Brian C. Wilson, Frédéric Leblond

Bibliographic record

VenueBiomedical Optics Express · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsOntario Institute for Cancer ResearchCentre Hospitalier de l’Université de MontréalUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsDiffuse optical imagingOptical coherence tomographyTomographyRadiologySensitivity (control systems)MedicineBiopsyPathology

Abstract

fetched live from OpenAlex

A brain needle biopsy procedure is performed for suspected brain lesions in order to sample tissue that is subsequently analysed using standard histopathology techniques. A common complication resulting from this procedure is brain hemorrhaging from blood vessels clipped off during tissue extraction. Interstitial optical tomography (iOT) has recently been introduced by our group as a mean to assess the presence of blood vessels in the vicinity of the needle. The clinical need to improve safety requires the detection of blood vessels within 2 mm from the outer surface of the needle, since this distance is representative of the volume of tissue that is aspirated durirng tissue extraction. Here, a sensitivity analysis is presented to establish the intrinsic detection limits of iOT based on simulations and experiments using brain tissue phantoms. It is demonstrated that absorbers can be detected with diameters >300 μm located up to >2 mm from the biopsy needle core for bulk optical properties consistent with brain tissue.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.307
Teacher spread0.284 · 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 designBench or experimental
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

Citations14
Published2015
Admission routes2
Has abstractyes

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