Francisco Schlottmann1,2, Neal S Murty2, Marco G Patti2. 1. 1 Department of Surgery, Hospital Alemán of Buenos Aires , Buenos Aires, Argentina . 2. 2 Department of Surgery, Center for Esophageal Diseases and Swallowing, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
Abstract
BACKGROUND: A significant gap presently exists between box-lap and virtual-reality simulators and live surgery. Live animal and cadaver use has significant downsides. We have developed a high fidelity, real tissue simulator that allows training in laparoscopic foregut operations. METHODS: Our foregut surgery model is based on porcine tissue blocks that include lungs, heart, aorta, esophagus, diaphragm, stomach, duodenum, liver, and spleen. The tissue block is mounted in a human mannequin and perfused with artificial blood. The anterior abdominal wall is constructed so as to allow for laparoscopic or robotic surgical training. We sought to test the model with procedures performed by expert surgeons. After completing the procedures, face validity was measured by surgeon responses to a questionnaire defining the perceived relationship to real surgery. RESULTS: Five expert foregut surgeons performed laparoscopic Heller myotomy, Nissen fundoplication, and sleeve gastrectomy on the model. The simulator was rated as highly realistic in terms of tissue feel, instrument usage, and conduct of the operation for all three procedures. In addition, all surgeons felt the model could significantly shorten the learning curve for performing these procedures. CONCLUSIONS: The results of this study show that our simulation model, based on animal tissue blocks, offers a very realistic representation of laparoscopic foregut operations, thus achieving a high level of face validity. The model should be very useful for training surgeons in laparoscopic foregut procedures.
BACKGROUND: A significant gap presently exists between box-lap and virtual-reality simulators and live surgery. Live animal and cadaver use has significant downsides. We have developed a high fidelity, real tissue simulator that allows training in laparoscopic foregut operations. METHODS: Our foregut surgery model is based on porcine tissue blocks that include lungs, heart, aorta, esophagus, diaphragm, stomach, duodenum, liver, and spleen. The tissue block is mounted in a human mannequin and perfused with artificial blood. The anterior abdominal wall is constructed so as to allow for laparoscopic or robotic surgical training. We sought to test the model with procedures performed by expert surgeons. After completing the procedures, face validity was measured by surgeon responses to a questionnaire defining the perceived relationship to real surgery. RESULTS: Five expert foregut surgeons performed laparoscopic Heller myotomy, Nissen fundoplication, and sleeve gastrectomy on the model. The simulator was rated as highly realistic in terms of tissue feel, instrument usage, and conduct of the operation for all three procedures. In addition, all surgeons felt the model could significantly shorten the learning curve for performing these procedures. CONCLUSIONS: The results of this study show that our simulation model, based on animal tissue blocks, offers a very realistic representation of laparoscopic foregut operations, thus achieving a high level of face validity. The model should be very useful for training surgeons in laparoscopic foregut procedures.
Authors: José Cornejo; Jorge A Cornejo-Aguilar; Mariela Vargas; Carlos G Helguero; Rafhael Milanezi de Andrade; Sebastian Torres-Montoya; Javier Asensio-Salazar; Alvaro Rivero Calle; Jaime Martínez Santos; Aaron Damon; Alfredo Quiñones-Hinojosa; Miguel D Quintero-Consuegra; Juan Pablo Umaña; Sebastian Gallo-Bernal; Manolo Briceño; Paolo Tripodi; Raul Sebastian; Paul Perales-Villarroel; Gabriel De la Cruz-Ku; Travis Mckenzie; Victor Sebastian Arruarana; Jiakai Ji; Laura Zuluaga; Daniela A Haehn; Albit Paoli; Jordan C Villa; Roxana Martinez; Cristians Gonzalez; Rafael J Grossmann; Gabriel Escalona; Ilaria Cinelli; Thais Russomano Journal: Biomed Res Int Date: 2022-03-24 Impact factor: 3.411