Feasibility of Specular Reflection Imaging for Extraction of Neck Vessel Pressure Waveforms
Bibliographic record
Abstract
<p>Cardiovascular disease (CVD) is a leading cause of death worldwide and was responsible for 31% of all deaths in 2015. Changes in fluid pressures within the vessels of the circulatory system reflect the mechanical function of the heart. The jugular venous (JV) pulse waveform is an important clinical sign for assessing cardiac function. However, technology able to aid evaluation and interpretation are currently lacking. The goal of the current study was to develop a remote monitoring tool that aid clinicians in robust measurements of JV pulse waveforms. To address this need, we have developed a novel imaging modality, Specular Reflection Vascular Imaging (SRVI). The technology uses specular reflection for visualization of skin displacements caused by pressure pulsations in blood vessels. SRVI has been tested on 10 healthy volunteers. 10-seconds videos of the neck illuminated with a diffuse light source were captured at 250 fps. SRVI was able to identify and discriminate skin displacements caused by carotid artery and jugular vein pulsations to extract both carotid artery and jugular vein waveforms, making them easier to be visualized and interpreted. The method provided a 6-fold improvement in signal strength over a comparator remote PPG dataset. The current pilot study is a proof-of-concept demonstration of the potential of Specular Reflection Vascular Imaging for extraction of JV pulse waveforms.</p> <p> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".