Biomechanics–Machine Learning System for Surgical Gesture Analysis and Development of Technologies for Minimal Access Surgery. (October 2014)

• Record Type: Journal Article
• Authors: Cavallo, Filippo; Sinigaglia, Stefano; Megali, Giuseppe; Pietrabissa, Andrea; Dario, Paolo; Mosca, Franco; Cuschieri, Alfred
• Is Part Of: Surgical innovation. Volume 21:Number 5(2014:Oct.)
• Page Start: 504
• Page End: 512
• Publication Date: 2014-10
• Subjects: surgical gesture analysis -- biomechanical analysis of movement -- machine learning approach -- metrics and benchmarks -- ergonomics -- surgical robotics; Surgery, Operative -- Periodicals; Endoscopic surgery -- Periodicals; Laparoscopic surgery -- Periodicals; Surgical Procedures, Operative -- Periodicals; Surgical Procedures, Minimally Invasive -- Periodicals; Diffusion of Innovation -- Periodicals; Chirurgie opératoire -- Périodiques; Chirurgie endoscopique -- Périodiques; Chirurgie laparoscopique -- Périodiques; 617.91
• DOI: 10.1177/1553350613510612
• Languages: English
• ISSNs: 1553-3506

Abstract

Background . The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. Methods . The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. Results . The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon–robot interactions. Conclusions . Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master–slave manipulator systems.