Evidence map›Paper›PMID 41669767›Full record

ArticleInterdisciplinary cardiovascular and thoracic surgery2026

Objective Assessment of Surgical Skill Using Artificial Intelligence Hand Tracking in Cardiothoracic Training: A Feasibility Study.

Morsal Atazadah, Mounir Bourass, Samuel A Max, Ivo M Cilon, J Wolter A Oosterhuis, Laurent N A Coopmans, Robert J M Klautz, Jerry Braun, Edris A F Mahtab

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Article in Interdisciplinary cardiovascular and thoracic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Morsal AtazadahDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Mounir BourassDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Samuel A MaxDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Ivo M CilonDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
J Wolter A OosterhuisDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Laurent N A CoopmansDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Robert J M KlautzDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Jerry BraunDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.
Edris A F MahtabDepartment of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, ZA 2333, The Netherlands.ORCID 0000-0003-2647-5509

Funding

E.A.F. Mahtab and S.A. Max
6 · The paper itself

Abstract

objectivesMeasuring surgical competency is essential for surgical residents to ensure patient safety. Traditional assessment tools rely on subjective evaluation. This study evaluated whether artificial intelligence (AI)-based hand tracking can more objectively distinguish between levels of surgical competency and predict surgical years of experience versus traditional assessments.

methodsA total of 44 participants, including medical students, surgical residents, and surgical consultants, performed transcutaneous suturing, intracutaneous suturing, and surgical knot tying. Videos of intracutaneous suturing were scored using the objective structured assessment of technical skills (OSATS). Hand movements were analysed using AI tracking software to extract coordinates to measure velocity, pathlength, and jerk. Linear regression models predicted experience years using procedural time and OSATS in combination with hand tracking metrics.

resultsHand tracking metrics varied mainly between medical students and more experienced groups. Traditional assessment tools (procedural time, OSATS) could predict experience years during training, with an adjusted coefficient of determination (R2) ranging from 0.537 to 0.638, dependent on procedure type. Hand tracking variables identified multiple significant predictors for years of experience, with an adjusted R2 of 0.540-0.712, which outperformed the traditional tools in each procedure. Combining all assessment tools (time, OSATS, and hand tracking) gave the best predictive value, with an adjusted R2 ranging from 0.540 to 0.809, with velocity, pathlength, jerk, and acceleration as significant predictors.

conclusionsAI-based hand tracking provides a new method for objective, reproducible measures of surgical skills. Incorporating hand tracking metrics enhances prediction of surgical experience, and supports standardized as well as objective evaluation of skills assessment in surgical training.

Indexed as

Artificial IntelligenceCardiac Surgical ProceduresClinical CompetenceEducation, Medical, GraduateInternship and ResidencySuture TechniquesFeasibility StudiesFemaleHandHumansIntelligent SystemsMaleMotion CaptureMotor Skillsartificial intelligencecardiothoracic surgeryhand trackingskills assessmentsurgical competencysurgical trainingtechnical skills

Identifiers

PMID41669767
PMCPMC12953238

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