Evidence map›Paper›PMID 31605351›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches.

Fernando Pérez-Escamirosa, Antonio Alarcón-Paredes, Gustavo Adolfo Alonso-Silverio, Ignacio Oropesa, Oscar Camacho-Nieto, Daniel Lorias-Espinoza, Arturo Minor-Martínez

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 6% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Developing and evaluating image-based metrics for open surgical suturing skill assessment.Global surgical education : journal of the Association for Surgical Education · 2026
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  9. Review
  10. A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data.International journal of computer assisted radiology and surgery · 2022
    Article
  11. Article
  12. Objective assessment of laparoscopic targeting skills using a Short-Time Power of Difference (STPOD) method.International journal of computer assisted radiology and surgery · 2022
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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 at 4 institutions in 2 countries.

Fernando Pérez-EscamirosaInstituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Circuito Exterior S/N, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico.ORCID http://orcid.org/0000-0001-7886-8692
Antonio Alarcón-ParedesLaboratory of Computing Technologies and Electronics, Faculty of Engineering, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, 39070, Chilpancingo, Guerrero, Mexico. aalarcon@uagro.mx.ORCID http://orcid.org/0000-0002-9785-1252
Gustavo Adolfo Alonso-SilverioLaboratory of Computing Technologies and Electronics, Faculty of Engineering, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, 39070, Chilpancingo, Guerrero, Mexico.ORCID http://orcid.org/0000-0002-2699-140X
Ignacio OropesaBiomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain.ORCID http://orcid.org/0000-0003-4560-285X
Oscar Camacho-NietoIntelligent Computing Laboratory, Centro de Innovación y Desarrollo Tecnológico en Computación (CIDETEC-IPN), Av. Juan de Dios Bátiz, Col. Nueva Industrial Vallejo, 07700, Ciudad de México, Mexico.ORCID http://orcid.org/0000-0003-1142-5277
Daniel Lorias-EspinozaDepartment of Electrical Engineering, Bioelectronics Section, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Ciudad de México, Mexico.ORCID http://orcid.org/0000-0003-2260-385X
Arturo Minor-MartínezDepartment of Electrical Engineering, Bioelectronics Section, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN 2508, Col. San Pedro Zacatenco, 07360, Ciudad de México, Mexico.ORCID http://orcid.org/0000-0001-5365-5957
Instituto Politécnico Nacional · MXUniversidad Autónoma de Guerrero · MXUniversidad Autónoma de la Ciudad de México · MXUniversidad Politécnica de Madrid · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe determination of surgeons' psychomotor skills in minimally invasive surgery techniques is one of the major concerns of the programs of surgical training in several hospitals. Therefore, it is important to assess and classify objectively the level of experience of surgeons and residents during their training process. The aim of this study was to investigate three classification methods for establishing automatically the level of surgical competence of the surgeons based on their psychomotor laparoscopic skills.

methodsA total of 43 participants, divided into an experienced surgeons group with ten experts (> 100 laparoscopic procedures performed) and non-experienced surgeons group with 24 residents and nine medical students (< 10 laparoscopic procedures performed), performed three tasks in the EndoViS training system. Motion data of the instruments were captured with a video-tracking system built into the EndoViS simulator and analyzed using 13 motion analysis parameters (MAPs). Radial basis function networks (RBFNets), K-star (K*), and random forest (RF) were used for classifying surgeons based on the MAPs' scores of all participants. The performance of the three classifiers was examined using hold-out and leave-one-out validation techniques.

resultsFor all three tasks, the K-star method was superior in terms of accuracy and AUC in both validation techniques. The mean accuracy of the classifiers was 93.33% for K-star, 87.58% for RBFNets, and 84.85% for RF in hold-out validation, and 91.47% for K-star, 89.92% for RBFNets, and 83.72% for RF in leave-one-out cross-validation.

conclusionsThe three proposed methods demonstrated high performance in the classification of laparoscopic surgeons, according to their level of psychomotor skills. Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscopic box trainers.

Indexed as

Clinical CompetenceEducation, MedicalFemaleHumansLaparoscopyMalePsychomotor PerformanceStudents, MedicalSurgeonsClassificationLaparoscopic surgeryMotion analysisObjective assessmentTrainingVideo-based tracking

Identifiers

PMID31605351
OpenAlexW2979474899

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.