首页|University Hospital Ghent Reports Findings in Robotics (Standardized porcine int egrated robotic inguinal hernia training: the SPIRIT model)

University Hospital Ghent Reports Findings in Robotics (Standardized porcine int egrated robotic inguinal hernia training: the SPIRIT model)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting originating from Ghent, Belgium, by N ewsRx correspondents, research stated, "Implementing a robotic system for minima lly invasive surgical procedures necessitates a comprehensive training regimen. This involves not only mastering the technological aspects of the robotic system but also enhancing surgical proficiency in manipulating robotic instruments." Our news editors obtained a quote from the research from University Hospital Ghe nt, "Furthermore, procedural expertise in specific surgeries is critical. Minima lly invasive inguinal hernia repair is particularly suitable as an initial proce dure for human application. The development of a comprehensive training model fo r this type of repair is a crucial element of such an educational pathway. Anato mical dissections were carried out on pigs to assess both the similarities and d ifferences between pig and human anatomy. A structured minimally invasive inguin al hernia repair was performed to determine the suitability of the porcine ingui nal region for training purposes. A detailed anatomical description of the porci ne inguinal region is outlined, to provide a framework for assessing the critica l view of the porcine myopectineal orifice. By integrating the human 'ten golden rules' for safe and effective minimally invasive inguinal hernia repair, the st andardized porcine integrated robotic inguinal hernia training (SPIRIT) model de scribes a step-by-step approach to practice surgical techniques in a realistic s etting."

GhentBelgiumEuropeEmerging Technol ogiesMachine LearningRoboticsRobotsSurgery

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.26)