Evidence map›Paper›PMID 41436615›Full record

Observational studySurgical endoscopy2026

Quantitative assessment of laparoscopic camera navigation skill: a retrospective cross-sectional study.

Annika Lindstrom Haughey, Shannon Barter, Cameron Reid, Steven Thornton, Sabino Zani, Katharine Louise Jackson, Brian Mann

Abstract readObservational StudyValidation Study
PubMed Publisher
In one paragraph

Observational study in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Annika Lindstrom HaugheyDepartment of Mechanical Engineering, Duke University, MEMS Dept. Box 90300, Durham, NC, 27708, USA. annika.haughey@duke.edu.ORCID 0009-0003-8926-8838
Shannon BarterDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA.
Cameron ReidDepartment of Mechanical Engineering, Duke University, MEMS Dept. Box 90300, Durham, NC, 27708, USA.
Steven ThorntonDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA.
Sabino ZaniDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA.
Katharine Louise JacksonDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA.
Brian MannDepartment of Mechanical Engineering, Duke University, MEMS Dept. Box 90300, Durham, NC, 27708, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate whether a low-cost, automated tool using trajectory-based metrics and real-time computer vision can objectively assess laparoscopic camera navigation (LCN) skill and distinguish between levels of surgical experience. Laparoscopic camera navigation (LCN) is essential to the safety and success of minimally invasive surgery. Despite its importance, LCN is often overlooked in formal training and lacks standardized assessment methods. Existing tools are labor-intensive, subjective, and not scalable, creating a need for efficient, objective evaluation strategies.

methodsThis cross-sectional observational validation study was conducted from January to April 2025 in a single-site academic surgical training laboratory. Fifty-nine participants (6 expert surgeons, 12 senior residents, 26 junior residents, and 15 novices) completed a standardized LCN task using a custom-designed maze and a 30° laparoscope in a bench-top trainer. Real-time computer vision detected targets, while an electromagnetic tracking system recorded the laparoscope's trajectory. Five automated performance metrics were collected: space coverage, dimensionless jerk, rotational smoothness, idle time, and total task time. A principal component analysis (PCA) was used to generate a composite performance score (0-100).

resultsAll participants completed the task. Experts demonstrated lower space coverage (121 ± 31 cm

conclusionsThis automated, low-cost tool reliably quantified LCN skill across training levels using interpretable metrics. Its objectivity and scalability support integration into surgical education for formative feedback and standardized assessment.

Indexed as

Clinical CompetenceLaparoscopySurgery, Computer-AssistedAdultCross-Sectional StudiesFemaleHumansInternship and ResidencyMaleRetrospective StudiesTask Performance and AnalysisComputer visionKinematic analysisLaprscopic camera nagivationPerformance metricsSurgical education and trainingSurgical simulation

Identifiers

PMID41436615

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.