Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.
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...
| Publicado en: | Surgical Innovation Vol. 25; no. 4; pp. 380 - 389 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130845289&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130845289 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15533506 16AH jtl: Surgical Innovation issn: 15533506 maglogo: Y pubinfo: dt: Aug2018 vid: 25 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 130845289 130845289 NLM29809097 10.1177/1553350618777045 NLM29809097 130845289 ppf: 380 ppct: 9 formats: tig: atl: Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills. aug: au: Alonso-Silverio, Gustavo A. Pérez-Escamirosa, Fernando Bruno-Sanchez, Raúl Ortiz-Simon, José L. Muñoz-Guerrero, Roberto Minor-Martinez, Arturo Alarcón-Paredes, Antonio affil: Universidad Autónoma de Guerrero, Chilpancingo, Guerrero, México sug: subj: Psychomotor Performance Laparoscopy Education Neural Networks (Computer) Task Performance and Analysis Education, Medical ROC Curve Students, Medical ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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