Treffer: Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

Title:
Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.
Authors:
Alonso-Silverio GA; 1 Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México., Pérez-Escamirosa F; 2 Universidad Nacional Autónoma de México UNAM, Ciudad de México, México., Bruno-Sanchez R; 1 Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México., Ortiz-Simon JL; 3 Instituto Tecnológico de Nuevo Laredo, Tamaulipas, México., Muñoz-Guerrero R; 4 Instituto Politécnico Nacional, Ciudad de México, México., Minor-Martinez A; 4 Instituto Politécnico Nacional, Ciudad de México, México., Alarcón-Paredes A; 1 Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México.
Source:
Surgical innovation [Surg Innov] 2018 Aug; Vol. 25 (4), pp. 380-388. Date of Electronic Publication: 2018 May 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Sage Publications Country of Publication: United States NLM ID: 101233809 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-3514 (Electronic) Linking ISSN: 15533506 NLM ISO Abbreviation: Surg Innov Subsets: MEDLINE
Imprint Name(s):
Original Publication: Thousand Oaks, CA : Sage Publications
MeSH Terms:
Neural Networks, Computer* , Virtual Reality*, Laparoscopy/*education , Psychomotor Performance/*physiology, Education, Medical ; Humans ; ROC Curve ; Students, Medical ; Task Performance and Analysis
Contributed Indexing:
Keywords: augmented reality; classification of MIS skills; laparoscopy box simulator; objective assessment
Entry Date(s):
Date Created: 20180530 Date Completed: 20181214 Latest Revision: 20191210
Update Code:
20250114
DOI:
10.1177/1553350618777045
PMID:
29809097
Database:
MEDLINE

Weitere Informationen

Background: A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language.
Methods: Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument. An artificial neural network (ANN) was trained to learn from experts and nonexperts' behavior for pattern cutting task, whereas the assessment of transferring task was performed using a preestablished threshold. Four expert surgeons in laparoscopic surgery, from hospital "Raymundo Abarca Alarcón," constituted the experienced class for the ANN. Sixteen trainees (10 medical students and 6 residents) without laparoscopic surgical skills and limited experience in minimal invasive techniques from School of Medicine at Universidad Autónoma de Guerrero constituted the nonexperienced class. Data from participants performing 5 daily repetitions for each task during 5 days were used to build the ANN.
Results: The participants tend to improve their learning curve and dexterity with this laparoscopic training system. The classifier shows mean accuracy and receiver operating characteristic curve of 90.98% and 0.93, respectively. Moreover, the ANN was able to evaluate the psychomotor skills of users into 2 classes: experienced or nonexperienced.
Conclusion: We constructed and evaluated an affordable laparoscopic trainer system using computer vision, augmented reality, and an artificial intelligence algorithm. The proposed trainer has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.