378 Workload Assessment of Suturing Tasks and AI-Task Classification Using Electromyography

Abstract

Abstract Aim It is well established that excess surgeon workload results in poorer performance. Workload can be divided into physical workload (PWL) and cognitive workload (CWL). The study aims are twofold: 1. Assess how the subjective PWL of suturing is affected by varying PWL and CWL 2. Develop an AI-classification algorithm for task detection using electromyography (EMG). Method Novices were trained in interrupted suturing and asked to suture under four conditions of varied workload. Tasks included were: 1. Control, 2. Different suture material (Silk), 3. Simultaneously undertaking a neurocognitive task and 4. Suturing at depth. Subjective workload was recorded using a validated workload questionnaire (SURG-TLX). EMG sensors recorded physiological data from the biceps brachii and extensor ulnar carpi. A convolutional neural network (CNN) was created for AI-task classification using EMG data. Results The depth task had the highest subjective workload. Subjective depth raw workload was significantly higher than Control (p=<0.01) and Silk (p=<0.001) but comparable to the neurocognitive task. Subjective depth weighted workload was significantly higher than the neurocognitive task (p=<0.05). Subjective depth adjusted workload was significantly higher than the neurocognitive and silk tasks (both p=<0.05). The CNN training accuracy was 76.4% (95%CI 75.5-77.4) and had a classification accuracy of 76.5% (95%CI 75.7-77.2). Conclusions The relationship between PWL and CWL on subjective PWL is complex. Despite this, the CNN was able to differentiate between tasks using objective EMG data with good accuracy. This CNN has potential to characterise CWL which can be further developed to detect excess workload to improve surgical training through objective benchmarks.