Neural-Network-Based Heart Motion Prediction for Ultrasound-Guided Beating-Heart Surgery
Bibliographic record
Abstract
A neural-network-based heart motion prediction method is proposed for ultrasound-guided beating-heart surgery to compensate for time delays caused by ultrasound (US) image acquisition and processing. Such image processing is needed for tracking heart tissue in US images, which is itself a requirement for beating-heart surgery. Once the heart tissue is tracked in US images, a recurrent neural network (NN) is employed to learn how to predict the motion of the tracked heart motion in order to compensate for the delays introduced in the initial US image processing step. To verify the feasibility of predicting both simple and complex heart motions, the NN is tested with two types of heart motion data: (i) fixed heart rate and maximum amplitude, and (ii) varying heart rate and maximum amplitude. Also, the NN was tested for different prediction horizons and showed effectiveness for both small and large delays. The heart motion prediction results using NN are compared to the results using an extended Kalman filter (EKF) algorithm. Using NN, the mean absolute error and the root mean squared error between the predicted and the actually tracked heart motions are roughly 60% smaller than those achieved by using the EKF. Moreover, the NN is able to predict the heart position up to 1000 ms in advance, which significantly exceeds the typical US image acquisition/processing delays for this application (160 ms in our tests). Overall, the NN predictor shows significant advantages (higher accuracy and longer prediction horizon) compared to the EKF predictor.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".