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Development and validation of the placenta-QUS model for the detection of placenta-mediated diseases using quantitative ultrasound measurements: An Ex Vivo proof-of-concept study

2024· article· en· W4404214317 on OpenAlexafffundabout
Farah Deeba, Ricky Hu, Victoria A. Lessoway, Jefferson Terry, D. Pugash, Chantal Mayer, Jennifer A. Hutcheon, Robert Rohling

Bibliographic record

VenuePlacenta · 2024
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsB.C. Women's Hospital & Health CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMicrosoft ResearchMicrosoftSchlumberger Foundation
KeywordsPlacentaEx vivoObstetricsProof of conceptIn vivoMedicineComputer scienceFetusPregnancyBiology

Abstract

fetched live from OpenAlex

Placenta-mediated diseases are associated with structural changes in the placenta. Quantitative Ultrasound (QUS) imaging measures the acoustic properties of the tissue, which are correlated to the underlying tissue structure. We aimed to develop and validate a diagnostic prediction model using QUS measurements for pre-eclampsia (PE) and small-for-gestational-age (SGA) fetuses/neonates. For this prospective case-control study, placentas were collected from a group of women who delivered via cesarean section at BC Women's Hospital, Vancouver, Canada. Ultrasound data were collected and processed to compute three QUS parameters, namely, attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC), and effective scatterer diameter (ESD) from the placentas. We developed a logistic regression model using QUS parameters as predictors. The primary outcome was the occurrence of SGA and PE. The dataset consisted of 47 placentas, of which 25 placentas were complicated by SGA/PE. The final placenta-QUS model included quadratic and interaction terms of ACE, IBC, and ESD parameters. The placenta-QUS model was well-calibrated, with a calibration slope of 0.99 (0.57–1.05) and a calibration intercept of 0.003 (−0.02 − 0.22). The model predicted the SGA/PE complicated pregnancies with an apparent Area Under the Receive Operating Characteristic Curve (AUROC) of 0.89 (95 % CI: 0.78–0.98). The optimism-adjusted AUROC was 0.88 (95 % CI: 0.78–0.98). A model for SGA and PE has been developed using QUS measures from the placenta ex vivo . The model showed promising performance in detecting SGA/PE. Future studies will be performed to assess the model performance using QUS measures in utero . • We developed a model, placenta-QUS, using quantitative ultrasound of the placenta ex vivo to detect placenta-mediated diseases. • The placenta-QUS model distinguishes between healthy pregnancies and pregnancies with small-for-gestational-age fetuses and preeclampsia. • Quantitative Ultrasound measurements obtained from the placenta can be novel biomarkers of pregnancy-related complications.

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 score0.461
Threshold uncertainty score0.430

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 head0.125
GPT teacher head0.340
Teacher spread0.215 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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