SWAVE 2.0 Imaging of Placental Elasticity and Viscosity: Potential Biomarkers for Placenta-Mediated Disease Detection
Bibliographic record
Abstract
Pregnancy complications such as pre-eclampsia (PE) and intrauterine growth restriction (IUGR) are associated with structural and functional changes in the placenta. Different elastography techniques with an ability to assess the mechanical properties of tissue can identify and monitor the pathological state of the placenta. Currently available elastography techniques have been used with promising results to detect placenta abnormalities; however, limitations include inadequate measurement depth and safety concerns from high negative pressure pulses. Previously, we described a shear wave absolute vibro-elastography (SWAVE) method by applying external low-frequency mechanical vibrations to generate shear waves and studied 61 post-delivery clinically normal placentas to explore the feasibility of SWAVE for placental assessment and establish a measurement baseline. This next phase of the study, namely, SWAVE 2.0, improves the previous system and elasticity reconstruction by incorporating a multi-frequency acquisition system and using a 3-D local frequency estimation (LFE) method. Compared with its 2-D counterpart, the proposed system using 3-D LFE was found to reduce the bias and variance in elasticity measurements in tissue-mimicking phantoms. In the aim of investigating the potential of improved SWAVE 2.0 measurements to identify placental abnormalities, we studied 46 post-delivery placentas, including 26 diseased (16 IUGR and 10 PE) and 20 normal control placentas. By use of a 3.33-MHz motorized curved-array transducer, multi-frequency (80,100 and 120 Hz) elasticity measures were obtained with 3-D LFE, and both IUGR (15.30 ± 2.96 kPa, p = 3.35 e –5 ) and PE (12.33 ± 4.88 kPa, p = 0.017) placentas were found to be significantly stiffer compared with the control placentas (8.32 ± 3.67 kPa). A linear discriminant analysis (LDA) classifier was able to classify between healthy and diseased placentas with a sensitivity, specificity and accuracy of 87%, 78% and 83% and an area under the receiver operating curve of 0.90 (95% confidence interval: 0.8–0.99). Further, the pregnancy outcome in terms of neonatal intensive care unit admission was predicted with a sensitivity, specificity and accuracy of 70%, 71%, 71%, respectively, and area under the receiver operating curve of 0.78 (confidence interval: 0.62–0.93). A viscoelastic characterization of placentas using a fractional rheological model revealed that the viscosity measures in terms of viscosity parameter n were significantly higher in IUGR (2.3 ± 0.21) and PE (2.11 ± 0.52) placentas than in normal placentas (1.45 ± 0.65). This work illustrates the potential relevance of elasticity and viscosity imaging using SWAVE 2.0 as a non-invasive technology for detection of placental abnormalities and the prediction of pregnancy outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".