Physiological Organ Motion Prediction and Compensation Based on Multirate, Delayed, and Unregistered Measurements in Robot-Assisted Surgery and Therapy
Bibliographic record
Abstract
Physiological motion makes performing a surgical or therapeutic procedure more difficult for the physician. In heart surgery, the heart is stopped as it is too difficult for the surgeon to follow the heart's beating motion and perform a surgical task. In radiation therapy, respiration causes the cancerous tissue to move, rendering the therapy less effective and possibly damaging to healthy tissue. This paper focuses on controlling a robot, which is used to perform the surgery or therapy, to compensate for the physiological motion along the surgical tool's axis such that the point of interest (POI) on the organ becomes stationary relative to the robot. The difficulty in creating such a system lies in the measurement of the POI's and robot's positions via different sensors that are unregistered, have different measurement rates, and have data acquisition and processing delays. This paper presents the Kalman-filter-based estimation of the organ motion despite the large data acquisition/processing delays and low update rates inherent in some measurements used for robot control in robot-assisted surgeries and therapies. This paper also proposes control systems that compensate for the organ motion despite the delayed, multirate, and unregistered sensor data allowing the physician to perform a therapeutic or surgical procedure with a teleoperated robot on a seemingly stationary POI.
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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 | 0.000 | 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 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".