Instrumental Mechanoreceptoric Palpation in Gastrointestinal Surgery

Abstract

Background and Aims Small gastric or colorectal tumours can be visually undetectable during laparoscopic surgeries, and available methods still do not provide a 100% localisation rate. Thus, new methods for further improvements in tumour localisation are highly desirable. In this study, we evaluated the usage of the Medical Tactile Endosurgical Complex (MTEC) in gastrointestinal surgery for localisation of tumours. The MTEC provides the possibility of instrumental mechanoreceptoric palpation, which serves as an analogue of conventional manual palpation. Methods Ninety-six elective surgeries were performed, including 48 open surgeries, 43 laparoscopies, and 5 robot-assisted surgeries. The 20 mm version of the MTEC tactile mechanoreceptor was used in open surgeries, and the 10 mm version in laparoscopic and robot-assisted surgeries. Results The mean time of instrumental mechanoreceptoric palpation was 3 minutes 12 seconds for open surgeries, which constituted the early stage of the learning curve, and 3 minutes 34 seconds for laparoscopic surgeries. No side effects or postoperative complications related to instrumental mechanoreceptoric palpation were observed, and this procedure provided data sufficient for tumour localisation in more than 95% of cases. Conclusion Instrumental mechanoreceptoric palpation performed using MTEC is a simple, safe, and reliable method for tumour localisation in gastrointestinal laparoscopic surgery.

NP-Hardness of the Problem of Optimal Box Positioning

“…The algorithm was inspired by the apparatus of maximum interval pattern concepts (see, e.g., [4,5]), a technique that allows one to select patterns from fuzzy contexts. This approach was successfully applied to the dataset of tactile images registered by the Medical Tactile Endosurgical Complex [6][7][8], which allows intraoperative tactile examination of tissues. Comparison of the proposed clustering approach with the conventional k-means clustering resulted in a statistically significant advantage of the proposed method over k-means in clustering quality.…”