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Record W2985048797 · doi:10.1002/jbio.201960083

Full‐field swept‐source optical coherence tomography and neural tissue classification for deep brain imaging

2019· article· en· W2985048797 on OpenAlex
Ilan Felts Almog, Fu‐Der Chen, Sühan Senova, Anton Fomenko, Elise Gondard, Wesley D. Sacher, Andrés M. Lozano, Joyce K. S. Poon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biophotonics · 2019
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersUniversity of Toronto
KeywordsOptical coherence tomographyNeuroimagingTomographyCoherence (philosophical gambling strategy)Optical tomographyBiomedical engineeringDeep brain stimulationBrain tumorPreclinical imagingOpticsComputer scienceNeuroscienceArtificial intelligenceMedicinePhysicsIn vivoPathologyBiology

Abstract

fetched live from OpenAlex

Optical coherence tomography can differentiate brain regions with intrinsic contrast and at a micron scale resolution. Such a device can be particularly useful as a real-time neurosurgical guidance tool. We present, to our knowledge, the first full-field swept-source optical coherence tomography system operating near a wavelength of 1310 nm. The proof-of-concept system was integrated with an endoscopic probe tip, which is compatible with deep brain stimulation keyhole neurosurgery. Neuroimaging experiments were performed on ex vivo brain tissues and in vivo in rat brains. Using classification algorithms involving texture features and optical attenuation, images were successfully classified into three brain tissue types.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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