GUZMAN MONTOTO, JOSE IGNACIO
Thumbnail Image
Preferred name
GUZMAN MONTOTO, JOSE IGNACIO
Main Affiliation
Email
jguzman@udd.cl
ORCID
Scopus Author ID
58617832500

Research Output

2024 2024 2023 2023 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0

Filters

2
Reset filters

Settings
Now showing 1 - 2 of 2
No Thumbnail Available
Publication

Designing a Novel VR Simulator for Core Laparoscopic Skills and Assessing Its Construct Validity via Machine Learning

2024 , GUZMAN MONTOTO, JOSE IGNACIO , Boris Marinkovic Gomez , RODRÍGUEZ BELTRÁN, CAMILO IGNACIO , HERRERA MARÍN, MAURICIO RENÉ

View
No Thumbnail Available
Publication

New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries

2023 , GUZMAN MONTOTO, JOSE IGNACIO , HERRERA MARÍN, MAURICIO RENÉ , RODRÍGUEZ BELTRÁN, CAMILO IGNACIO

This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.

View