A novel software program for detection of potential air emboli during cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Risks associated with air emboli introduced during cardiac surgery have been highlighted by reports of postoperative neuropsychological dysfunction, myocardial dysfunction, and mortality. Presently, there are no standard effective methods for quantifying potential emboli in the bloodstream during cardiac surgery. Our objective was to develop software that can automatically detect and quantify air bubbles within the ascending aorta and/or cardiac chambers during cardiac surgery in real time. FINDINGS: We created a software algorithm ("Detection of Emboli using Transesophageal Echocardiography for Counting, Total volume, and Size estimation", or DETECTS™) to identify and measure potential emboli present during cardiac surgery using two-dimensional ultrasound. An in vitro experiment was used to validate the accuracy of DETECTS™ at identifying and measuring air emboli. An experimental rig was built to correlate the ultrasound images to high definition camera images of air bubbles created in water by an automatic bubbler system. There was a correlation between true bubble size and the size reported by DETECTS™ in our in vitro experiment (r = 0.76). We also tested DETECTS™ using TEE images obtained during cardiac surgery, and provide visualization of the software interface. CONCLUSIONS: While monitoring the heart during cardiac surgery using existing ultrasound technology and DETECTS™, the operative team can obtain real-time data on the number and volume of potential air emboli. This system will potentially allow de-airing techniques to be evaluated and improved upon. This could lead to reduced air in the cardiac chambers after cardiopulmonary bypass, possibly reducing the risk of neurological dysfunction following cardiac surgery.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".