A Deep Learning Force Estimator System for Intracardiac Catheters
Bibliographic record
Abstract
Having a real sense of the applied force in catheterization procedures can help surgeons with proper treatment for cardiovascular diseases. Using sensors is not common because of the limitations of catheters and complications related to the safety of patients. In this regard, a sensor free method can be deemed as a safe solution, in which it uses available equipment in the real operation room. In this work, we propose a deep learning method to estimate the contact forces directly from the catheters' image tip without embedding further sensors. A convolutional neural network extracts the catheter's deflections through input images and translates them into the corresponding forces. The architecture of the proposed model has been inspired by the ResNet graph so as to perform a regression. The model can make predictions based on the input images without utilizing any feature extraction or preprocessing steps. An experimental setup was designed and implemented to simulate catheter ablation therapy. Evaluation results show that the proposed method is able to elicit a robust model from the given dataset and approximate the force with proper accuracy. Opting RMSE as the preferred performance metric, the model reached 0.028 N and 0.023 N in estimation error in the x and y direction on the test data set, respectively.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".