A Wireless Wearable Doppler Ultrasound Detects Changing Stroke Volume: Proof-of-Principle Comparison with Trans-Esophageal Echocardiography during Coronary Bypass Surgery
Bibliographic record
Abstract
Background: A novel, wireless, ultrasound biosensor that adheres to the neck and measures real-time Doppler of the carotid artery may be a useful functional hemodynamic monitor. A unique experimental set-up during elective coronary artery bypass surgery is described as a means to compare the wearable Doppler to trans-esophageal echocardiography (TEE). Methods: A total of two representative patients were studied at baseline and during Trendelenburg position. Carotid Doppler spectra from the wearable ultrasound and TEE were synchronously captured. Areas under the receiver operator curve (AUROC) were performed to assess the accuracy of changing common carotid artery velocity time integral (ccVTI∆) at detecting a clinically significant change in stroke volume (SV∆). Results: Synchronously measuring and comparing Doppler spectra from the wearable ultrasound and TEE is feasible during Trendelenburg positioning. In two representative cardiac surgical patients, the ccVTI∆ accurately detected a clinically significant SV∆ with AUROCs of 0.89, 0.91, and 0.95 when single-beat, 3-consecutive beat and 10-consecutive beat averages were assessed, respectively. Conclusion: In this proof-of-principle research communication, a wearable Doppler ultrasound system is successfully compared to TEE. Preliminary data suggests that the diagnostic accuracy of carotid Doppler ultrasonography at detecting clinically significant SV∆ is enhanced by averaging more cardiac cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".