MétaCan
Menu
Back to cohort
Record W4377294679 · doi:10.1364/boe.488845

Geometrically accurate real-time volumetric visualization of the middle ear using optical coherence tomography

2023· article· en· W4377294679 on OpenAlex
Joshua Farrell, Junzhe Wang, Dan MacDougall, Xiaojie Yang, Kimberly Brewer, Floor Couvreur, Nael Shoman, David P. Morris, Robert B. A. Adamson

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

VenueBiomedical Optics Express · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsOptical coherence tomographyField of viewImaging phantomTomographyOpticsMiddle earComputer scienceMedical imagingVisualizationComputer visionDistortion (music)Artificial intelligencePhysicsMedicineAnatomy

Abstract

fetched live from OpenAlex

We introduce a novel system for geometrically accurate, continuous, live, volumetric middle ear optical coherence tomography imaging over a 10.9 m m ×30 ∘ ×30 ∘ field of view (FOV) from a handheld imaging probe. The system employs a discretized spiral scanning (DC-SC) pattern to rapidly collect volumetric data and applies real-time scan conversion and lateral angular distortion correction to reduce geometric inaccuracies to below the system’s lateral resolution over 92% of the FOV. We validate the geometric accuracy of the resulting images through comparison with co-registered micro-computed tomography (micro-CT) volumes of a phantom target and a cadaveric middle ear. The system’s real-time volumetric imaging capabilities are assessed by imaging the ear of a healthy subject while performing dynamic pressurization of the middle ear in a Valsalva maneuver.

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 score0.913
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.013
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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