Role of Simulation-Based Training in Minimally Invasive and Robotic Colorectal Surgery

Can Computerized Simulation be Used to Assess Surgical Proficiency in Laparoscopic Colorectal Surgeries? A Systematic Review

INTRODUCTION

Computerized simulation (CS) of surgery in virtual reality (VR), augmented reality (AR) and mixed reality (MR) settings are used to teach foundational skills, but its applicability in advanced training is to be determined. This review aims to summarize the types of CS available for laparoscopic colorectal surgery (CRS) and its utility in assessment of proficiency.

METHODS

A systematic review of CS in laparoscopic CRS was done on PubMed, Embase, Scopus and Cochrane Library databases.

RESULTS

Eleven relevant observational studies were identified. The most common procedure simulated was laparoscopic colectomy. Assessment using performance metrics measured by the simulator such as path length moved by laparoscopic tools, procedure time and number of discrete movements had the most consistent differentiating ability between expert and non-expert cohorts. Surgeons fared similarly in proficiency scores in assessment with CS compared to assessment with traditional cadaveric or porcine models.

CONCLUSION

CS of laparoscopic CRS may be used in assessment of proficiency using performance metrics measuring economy of movement. CS may be a viable assessment tool in advanced surgical training, but further studies should assess utility of incorporating it as a formal assessment tool in training programs.

Artificial Intelligence With Robotics in Healthcare: A Narrative Review of Its Viability in India

This short review focuses on the emerging role of artificial intelligence (AI) with robotics in the healthcare sector. It may have particular utility for India, which has limited access to healthcare providers for a large growing population and limited health resources in rural India. AI works with an amalgamation of enormous amounts of data using fast and complex algorithms. This permits the software to quickly adapt the pattern of the data characteristics. It has the possibility to collide with most of the facets of the health system which may range from discovery to prediction and deterrence. The use of AI with robotics in the healthcare sector has shown a remarkable rising trend in the past few years. Functions like assistance with surgery, streamlining hospital logistics, and conducting routine checkups are some of the tasks that may be managed with great efficiency using artificial intelligence in urban and rural hospitals across the country. AI in the healthcare sector is advantageous in terms of ensuring exclusive patient care, safe working conditions where healthcare providers are at a lower risk of getting infected, and perfectly organized operational tasks. As the healthcare segment is globally recognized as one of the most dynamic and biggest industries, it tends to expedite development through modernization and original approaches. The future of this lucrative industry is looking forward to a great revolution aiming to create intelligent machines that work and respond like human beings. The future perspective of AI and robotics in the healthcare sector encompasses the care of elderly people, drug discovery, diagnosis of deadly diseases, a boost in clinical trials, remote patient monitoring, prediction of epidemic outbreaks, etc. However, the viability of using robotics in healthcare may be questionable in terms of expenditure, skilled workforce, and the conventional mindset of people. The biggest challenge is the replication of these technologies to the smaller towns and rural areas so that these facilities may reach the larger segment of the entire population of the country. This review aims to examine the adaptability and viability of these new technologies in the Indian scenario and identify the major challenges.

Factors affecting the learning curve in robotic colorectal surgery

Learning related to robotic colorectal surgery can be measured by surgical process (such as time or adequacy of resection) or patient outcome (such as morbidity or quality of life). Time based metrics are the most commonly used variables to assess the learning curve because of ease of analysis. With analysis of the learning curve, there are factors which need to be considered because they may have a direct impact on operative times or may be surrogate markers of clinical effectiveness (unrelated to times). Variables which may impact on operation time include surgery case mix, hybrid technique, laparoscopic and open colorectal surgery experience, robotic surgical simulator training, technology, operating room team, and case complexity. Multidimensional analysis can address multiple indicators of surgical performance and include variables such as conversion rate, complications, oncological outcome and functional outcome. Analysis of patient outcome and/or global assessment of robotic skills may be the most reliable methods to assess the learning curve.

Three-D-printed simulator for kidney transplantation

BACKGROUND

Three-Dimensional (3D) printing technology can be used to manufacture training platforms for surgeons. Kidney transplantation offers a suitable model, since it mostly entails vascular and ureteric anastomoses.

METHODS

A new simulation platform for surgical training in kidney transplantation was realized and validated in this study. A combination of different 3-D printing technology was used to reproduce the key anatomy of lower abdomen, of pelvis, and of a kidney graft, including their mechanical properties.

RESULTS

Thirty transplantations were performed by two junior trainees with no previous experience in the area. Analysis of the times required to perform the simulated transplantation showed that proficiency was reached after about ten cases, as indicated by a flattening of the respective curves that corresponded to a shortening of about 40% and 47%, respectively, of the total time initially needed to perform the whole simulated transplantation. Although an objective assessment of the technical quality of the anastomoses failed to show a significant improvement throughout the study, a growth in self-confidence with the procedure was reported by both trainees.

CONCLUSION

The quality of the presented simulation platform aimed at reproducing in the highest possible way a realistic model of the operative setting and proved effective in providing an integrated training environment where technical skills are enhanced together with a team-training experience. As a result the trainees' self-confidence with the procedure resulted enforced. Three-D--printed models can also offer pre-operative patient-specific training when anatomical variants are anticipated by medical imaging. An analysis of the costs related to the use of this platform is also provided and discussed.