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Surg Endosc. 2018 Jul;32(7):3096-3107. doi: 10.1007/s00464-018-6022-6. Epub 2018 Jan 18.

Interpretation of motion analysis of laparoscopic instruments based on principal component analysis in box trainer settings.

Author information

1
Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain. ioropesa@gbt.tfo.upm.es.
2
Department of Surgery, Faculty of Medicine, Universidad Nacional Autónoma de México (UNAM), Circuito Interior, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, 04510, Mexico City, Mexico.
3
Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, Carretera N-521, km 41.8, 10071, Cáceres, Spain.
4
Laparoscopy Unit, Jesús Usón Minimally Invasive Surgery Centre, Carretera N-521, km 41.8, 10071, Cáceres, Spain.
5
Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain.
6
Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), C/Monforte de Lemos 3-5, 28029, Madrid, Spain.
7
Department 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, Mexico City, Mexico.

Abstract

BACKGROUND:

Motion analysis parameters (MAPs) have been extensively validated for assessment of minimally invasive surgical skills. However, there are discrepancies on how specific MAPs, tasks, and skills match with each other, reflecting that motion analysis cannot be generalized independently of the learning outcomes of a task. Additionally, there is a lack of knowledge on the meaning of motion analysis in terms of surgical skills, making difficult the provision of meaningful, didactic feedback. In this study, new higher significance MAPs (HSMAPs) are proposed, validated, and discussed for the assessment of technical skills in box trainers, based on principal component analysis (PCA).

METHODS:

Motion analysis data were collected from 25 volunteers performing three box trainer tasks (peg grasping/PG, pattern cutting/PC, knot suturing/KS) using the EVA tracking system. PCA was applied on 10 MAPs for each task and hand. Principal components were trimmed to those accounting for an explained variance > 80% to define the HSMAPs. Individual contributions of MAPs to HSMAPs were obtained by loading analysis and varimax rotation. Construct validity of the new HSMAPs was carried out at two levels of experience based on number of surgeries.

RESULTS:

Three new HSMAPs per hand were defined for PG and PC tasks, and two per hand for KS task. PG presented validity for HSMAPs related to insecurity and economy of space. PC showed validity for HSMAPs related to cutting efficacy, peripheral unawareness, and confidence. Finally, KS presented validity for HSMAPs related with economy of space and knotting security.

CONCLUSIONS:

PCA-defined HSMAPs can be used for technical skills' assessment. Construct validation and expert knowledge can be combined to infer how competences are acquired in box trainer tasks. These findings can be exploited to provide residents with meaningful feedback on performance. Future works will compare the new HSMAPs with valid scoring systems such as GOALS.

KEYWORDS:

Box trainer; EVA tracking system; HSMAP; Motion analysis; Principal component analysis

PMID:
29349544
DOI:
10.1007/s00464-018-6022-6

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