LAparoscopic Skill and Kinematics is a laparoscopic peg-transfer dataset that pairs endoscopic video with electromagnetic measurements from two instruments, manual instrument annotations, and participant experience metadata.
Release status
Zenodo version 1.0 contains 37 recordings. The complete 115-recording collection used by the quantitative analysis will be added to the same concept DOI. This repository already provides the analysis code, aggregate results, feature definitions, and representative figures. It does not expose participant-level data from recordings that have not yet been released.
- Dataset guide
- Quantitative analysis
- Motion feature definitions
- Phase annotation protocol
- Analysis script
- Aggregate result tables
- Representative figures
The three cohorts used different collection settings. Raw measurements should therefore be interpreted within cohort or with cohort included explicitly in the statistical model.
| Cohort | Collection | Instrument channels | Frame rate | Public v1.0 | Complete collection |
|---|---|---|---|---|---|
| Paediatric | British Association of Paediatric Endoscopic Surgeons meeting, November 2024 | Position, orientation, relative jaw opening | 13 frames/s | 10 | 30 |
| Urology 1 | Urology boot camp, October 2023 | Position and orientation | 26 frames/s | 8 | 24 |
| Urology 2 | Urology boot camp, October 2024 | Position, orientation, relative jaw opening | 13 frames/s | 19 | 61 |
| Total | 37 | 115 |
The public release is organised as Urology 2 training data, Paediatric
validation data, and Urology 1 testing data. The names 7DOF2024,
BAPES2024, and 6DOF2023 are retained in files for compatibility.
In the analysis and figures, the acquisition channels are described as the
left tool and right tool, according to their usual side of entry in the
endoscopic image. These are image-side labels, not dominant-hand and
non-dominant-hand labels. The mapping was checked against annotated reference
frames from each cohort. Internal fields retain tool1_* and tool2_* names
for compatibility.
- Endoscopic video of a complete peg-transfer attempt.
- Three-dimensional tool position in millimetres for both instruments.
- Tool orientation as a unit quaternion for both instruments.
- A relative jaw-opening signal in the two seven-channel cohorts. This is a per-recording voltage-derived opening fraction, not an absolute jaw angle.
- Manual masks and instrument landmarks on selected video frames.
- Self-reported handedness and laparoscopic procedure experience where collected.
- Dense action-phase labels for the currently annotated subset.
The electromagnetic sensor is mounted near the instrument base and calibrated to estimate tool-tip position. The complete data structure and coordinate conventions are described in the dataset guide.
The companion analysis keeps four denominators separate.
| Analysis unit | Recordings | Study identifiers | Purpose |
|---|---|---|---|
| Complete inventory | 115 | 111 | Describe every available recording |
| Primary motion set | 107 | 107 | Motion inference without repeated identifiers |
| Phase inventory | 38 | 37 | Describe every densely annotated recording |
| Primary phase set | 34 | 34 | Phase comparisons without repeated identifiers |
The 38 phase-labelled recordings contain 425 placement-complete transfer cycles. The primary phase set contains 383 cycles.
The main findings are:
- No novice, intermediate, and expert procedure-count comparison remained significant after correction across 29 motion features.
- Eight features had modest within-cohort associations with lifetime procedure volume. Longer experience was associated with shorter analysed duration, lower normalised jerk, fewer speed peaks, shorter left-tool path length, faster right-tool movement, and bimanual correlation. The bimanual association varied by cohort; its random-effects sensitivity estimate was inconclusive.
- Cohort accounted for 88% and 92% of the variation in left- and right-tool speed, respectively. This shows why unadjusted pooling across collection settings is misleading.
- Data-derived lower, middle, and upper motion-score bands had negligible agreement with procedure-count groups (adjusted Rand index 0.019). These are descriptive motion strata, not clinical skill grades.
- Removing duration-dependent normalised jerk retained a related continuous score but changed many band assignments (adjusted Rand index 0.351). None of the six contextual duration or task-efficiency associations then survived correction.
- Models recovered the motion-score rule with high accuracy because the target was calculated from the same motion domains. This is a software consistency check and must not be interpreted as independent skill prediction.
Duration, movement smoothness, stop-start peaks, travel distance, and coordination between the tools are prioritised for prospective feedback validation. Raw speed, workspace size, jaw voltage, and phase fractions are less dependable as stand-alone indicators. The current results do not show that feedback on any one measurement improves learning, and they do not support instructing trainees simply to move faster or to use a smaller workspace.
Full methods, confidence intervals, corrected probability values, sensitivity analyses, and limitations are provided in the analysis guide.
The same experience measure can have different motion relationships in each
cohort. The top row shows bimanual correlation across all 107 primary
recordings. The lower row uses the phase-labelled subset to show mean cycle
duration. Corrected all-recording results are available in
all_trial_feature_statistics.csv.
Each colour shows the expert-minus-novice effect estimated within one cohort. The wide and sometimes inconsistent intervals show why a pooled effect can hide acquisition-specific uncertainty. The analysis therefore avoids treating the three cohorts as if their coordinate systems and equipment were interchangeable.
The coordination and control score is interpreted continuously. Duration and task efficiency are shown as contextual measurements rather than external clinical validation. A sensitivity analysis without normalised jerk shows why the categorical bands should not be treated as fixed performance grades.
Additional examples include the cohort inventory and phase timing analysis.
Create an environment and install the recorded package versions:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txtThe script uses the sibling BTPN-MT and AI-ELT paths by default. Other
locations can be supplied explicitly:
export LASK_CODE_ROOT=/path/containing/AI-ELT-and-BTPN-MT
export LASK_PHASE_CACHE=/path/to/phase_cache
export LASK_AI_ELT_ROOT=/path/to/AI-ELT
export LASK_ORIGIN_MOTION=/path/to/per_recording_kinematic_json
python scripts/build_scirep_analysis.pyExpected inputs and their schemas are listed in
data/README.md. The current Zenodo release does not yet
contain every cache needed to regenerate the 115-recording paper analysis.
Until the staged release is expanded, the repository provides the aggregate
outputs and file hashes produced by the complete internal collection.
Procedure volume is an experience measure, not a direct assessment of competence. The motion-score bands are relative partitions of two calculated motion domains. They are not scores from the Objective Structured Assessment of Technical Skills, Global Operative Assessment of Laparoscopic Skills, Global Evaluative Assessment of Robotic Skills, McGill Inanimate System for Training and Evaluation of Laparoscopic Skills, or Fundamentals of Laparoscopic Surgery.
One trained researcher annotated the action phases. Participant experience metadata were not visible during annotation. Differences in apparent confidence and fluency could still be perceived from the videos, but informal impressions of skill were not recorded and were not used to define phase labels. Independent phase annotation and expert rating remain planned validation work.
Please cite the dataset using the concept DOI so that the citation resolves to the latest release:
@dataset{Choudhry2026LASK,
title = {LASK: A Dataset for Laparoscopic Skill and 7-DoF Kinematics},
author = {Choudhry, Omar and Jones, Dominic},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.20752650},
url = {https://doi.org/10.5281/zenodo.20752650}
}The original dataset paper and related work are listed in
CITATION.cff.
The dataset and repository contents are released under the Creative Commons Attribution 4.0 licence. For questions, open a GitHub issue or contact Omar Choudhry, School of Computing, University of Leeds.