MétaCan
Menu
Back to cohort
Record W2967938123 · doi:10.1117/1.jbo.24.8.080902

Review of the state of the art in cardiovascular endoscopy imaging of atherosclerosis using photoacoustic techniques with pulsed and continuous-wave optical excitations

2019· review· en· W2967938123 on OpenAlexafffund
Sung Soo Choi, Andreas Mandelis

Bibliographic record

VenueJournal of Biomedical Optics · 2019
Typereview
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Ontario
KeywordsContinuous waveBiomedical engineeringMaterials scienceSignal processingOpticsPhotoacoustic imaging in biomedicineModality (human–computer interaction)RadarMedicinePhysicsComputer scienceLaserTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Intravascular photoacoustics (IV-PA) is an emerging atherosclerosis imaging modality that provides chemical-specific optical information of arterial walls with acoustic depth penetration and resolution. As lipid composition of atherosclerotic plaques is considered to be one of the primary indicators for plaque vulnerability, many IV-PA applications are calibrated so as to target plaque necrotic cores. Based on the mode of optical excitation and the corresponding signal processing technique, IV-PA is categorized into two different modalities. The pulse-based IV-PA has been the universal IV-PA imaging mode with its high peak power and straightforward time-domain signal processing technique. As an alternative, the low power continuous-wave (CW)-based IV-PA has been under intense development as a radar-like frequency-domain signal processing modality. The two state-of-the-art types of IV-PA are reviewed in terms of their physics and imaging capabilities, with major emphasis on frequency-swept CW-based IV-PA that has been recently introduced in the field.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.559
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.262
Teacher spread0.239 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations26
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Biomedical OpticsSame topicPhotoacoustic and Ultrasonic ImagingFrench-language works237,207