Two-level Fuzzy Logic Evaluation System for Surgeon's Hand Movement Using Object Detection

One significant aspect of surgical education and training is autonomous surgical skill assessment with feedback. In this paper, an autonomous two-level fuzzy logic assessment system for tracking and evaluation of laparoscopic instruments' tooltip movements for the FLS peg transfer task is proposed. The surgeon's left and right-hand movements are detected by using an Artificial Intelligence Network through instrument tooltip detection and position coordinates calculations. A first of its kind, custom laparoscopic box trainer dataset was built from experimental peg transfer task video recordings which were carried out by 9 doctors and OB/GYN residents, of the Homer Stryker M.D. School of Medicine, WMU, in the Intelligent Fuzzy Controllers Laboratory, WMU. A multi-class object detection algorithm, based on Deep Neural Networks, was developed.