AI-Powered Robust Interaction Force Control of a Cardiac Ultrasound Robotic System
Bibliographic record
Abstract
This article introduces a novel intelligent robust interaction force control method for a cardiac ultrasound robotic system (CURS), exploiting dual control loops and artificial intelligence (AI)-driven image feedback to enhance both image quality and patient safety during cardiac examinations. Unlike existing systems that use a constant interaction force, the proposed method adjusts the force based on ultrasound image feedback, which is critical for adapting to different cardiac views. The system employs an internal control loop, where the force feedback generates control commands (low-level controller), and an external control loop, where the feedback is processed through a convolutional neural network (CNN), named ultrasound-cardiac-feature-net (UCF-Net), determines the optimal force values (high-level controller). An adaptive filtered quasi-sliding mode controller (AFQSMC) manages both interaction force and probe’s position within a hybrid position/force control context, ensuring robustness against uncertainties and disturbances. Experimental evaluations on a cardiac phantom navigating main cardiac views demonstrate the superiority of the proposed approach over traditional constant force control. Moreover, AFQSMC achieves significant improvements in interaction force control, with enhancements ranging from 21.87% to 68.25% over traditional FQSMC, sliding mode control (SMC), and proportional-integral (PI) controllers, across quantitative metrics such as root mean square (RMS), standard deviation (STD), and Max, confirming its potential for improving cardiac examination performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".