Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills
Por:
Alonso-Silverio, Gustavo A., Perez-Escamirosa, Fernando, Bruno-Sanchez, Raul, Ortiz-Simon, Jose L., Munoz-Guerrero, Roberto, Minor-Martinez, Arturo, Alarcon-Paredes, Antonio
Publicada:
1 ago 2018
Ahead of Print:
1 ene 2018
Categoría:
Surgery
Resumen:
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 Alarcon, 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 Autonoma 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.
Filiaciones:
Alonso-Silverio, Gustavo A.:
Univ Autonoma Guerrero, Chilpancingo, Guerrero, Mexico
Perez-Escamirosa, Fernando:
Univ Nacl Autonoma Mexico, Ciudad De Mexico, Mexico
Bruno-Sanchez, Raul:
Univ Autonoma Guerrero, Chilpancingo, Guerrero, Mexico
Ortiz-Simon, Jose L.:
Inst Tecnol Nuevo Laredo, Nuevo Laredo, Tamaulipas, Mexico
Munoz-Guerrero, Roberto:
Inst Politecn Nacl, Ciudad De Mexico, Mexico
Minor-Martinez, Arturo:
Inst Politecn Nacl, Ciudad De Mexico, Mexico
Alarcon-Paredes, Antonio:
Univ Autonoma Guerrero, Chilpancingo, Guerrero, Mexico
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