Localized Muscular Fatigue in Robotic-Assisted Laparoscopic Surgery: Predictive Modeling Study
Bibliographic record
Abstract
Background Robotic-assisted surgery (RAS) has grown rapidly in recent decades, and several RAS procedures have become the standard. However, the physical and mental demands of minimally invasive surgery (MIS) techniques can lead to ergonomic shortcomings for surgeons. Advances in wearable technology and artificial intelligence favor the development of innovative solutions to analyze and improve ergonomic conditions during surgical practice. Objective The main objective is the development and validation of a predictive model of localized muscle fatigue from electromyography (EMG) data during conventional laparoscopic surgery (LAP) and RAS. Methods Four different tasks were performed on LAP and RAS: dissection, labyrinth, peg transfer, and suturing. A wireless EMG sensor system was used to record muscle activity. Joint analysis of the spectrum and analysis graphs was used to evaluate the localized muscle fatigue. A dataset was generated for each task as a function of surgeons’ expertise level and surgical type. Each dataset was scaled as preprocessing and divided into 2 datasets: 80% for training and 20% for testing. Multiple linear regression (MLR) and multilayer perceptron (MLP) were applied as predictive techniques and validated on all test datasets. R2 coefficient and root-mean-square error were used to measure the accuracy of the models. Results RAS showed less muscle fatigue for novice surgeons compared to LAP practice, although it was higher for expert surgeons. The predictive model achieved satisfactory R2 and root-mean-square error coefficients for all parameters extracted from the EMG signal, predicting with high accuracy localized muscle fatigue values. The MLR predictive model demonstrated superior performance relative to the MLP model. Conclusions Wearable technology and artificial intelligence techniques have been successfully applied for the development and validation of a novel predictive model based on MLR and MLP to predict localized muscle fatigue in MIS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".