Fuzzy logic supervisor – A surgical skills assessment system using multi-class detection of laparoscopic box-trainer instruments

Abstract

Recent developments in deep learning can be used in skill assessments for laparoscopic surgeons. In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Laparoscopic Surgical Box-Trainer allows surgery residents to train on specific skills that are not traditionally taught to them. This study aims to automatically detect the tips of laparoscopic instruments, localize a point, evaluate the detection accuracy to provide valuable assessment and expedite the development of surgery skills and assess the trainees’ performance using a Multi-Input-Single-Output Fuzzy Logic Supervisor system. The output of the fuzzy logic assessment is the performance evaluation for the surgeon, and it is quantified in percentages. Based on the experimental results, the trained SSD Mobilenet V2 FPN can identify each instrument at a score of 70% fidelity. On the other hand, the trained SSD ResNet50 V1 FPN can detect each instrument at the score of 90% fidelity, in each location within a region of interest, and determine their relative distance with over 65% and 80% reliability, respectively. This method can be applied in different types of laparoscopic tooltip detection. Because there were a few instances when the detection failed, and the system was designed to generate pass-fail assessment, we recommend improving the measurement algorithm and the performance assessment by adding a camera to the system and measuring the distance from multiple perspectives.