The use of augmented reality in laparoscopic surgical training: an overview
Bibliographic record
Abstract
Introduction: Laparoscopic surgery, a minimally invasive and intricate procedure, offers substantial risk reduction and numerous patient advantages.The increasing demand for this technique has forced the derivation of efficient training methods to cultivate a competent workforce.Though various evaluation methods and programmes are available to score and teach the vast and specific set of skills required, the predominant apprenticeship model, relying on patient interaction, results in a prolonged learning curve.Alternative training modalities, including human cadavers, box trainers, virtual reality (VR) simulators and augmented reality (AR) simulators, each possess distinct benefits and limitations.AR, a cutting-edge addition to laparoscopic surgical training, combines digital images and physical models, offering a unique blend of visual realism and haptic feedback.This study aims to provide an overview of laparoscopic training modalities and assess how augmented reality compares.Methodology: Reviewing 31 papers from diverse databases, findings were compiled and discussed.Results: Evaluation of current market simulators revealed variations in price, modules, assessment metrics and feedback method.ProMIS AR, validated for accurately assessing laparoscopic skills, exhibits subjective limitations.Comparatively, AR demonstrates faster skill acquisition and widespread preference.Discussion: While insufficient information hinders a decisive conclusion, AR simulation holds potential as the new gold standard for laparoscopic surgical training.Further research, encompassing a variety of simulators and modules, along with assessing mental and/or physical workload, will enhance understanding.AR's evolution and the increased literature exploring its capabilities promise to redefine laparoscopic surgical training, pending technological advancements for heightened clinical realism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".