Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Development of a Hand Motion-based Assessment System for Endotracheal Intubation Training.

Endotracheal intubation (ETI) is a procedure to manage and secure an unconscious patient's airway. It is one of the most critical skills in emergency or intensive care. Regular training and practice are required for medical providers to maintain proficiency. Currently, ETI training is assessed by human supervisors who may make inconsistent assessments. This study aims at developing an automated assessment system that analyzes ETI skills and classifies a trainee into an experienced or a novice immediately after training. To make the system more available and affordable, we investigate the feasibility of utilizing only hand motion features as determining factors of ETI proficiency. To this end, we extract 18 features from hand motion in time and frequency domains, and also 12 force features for comparison. Subsequently, feature selection algorithms are applied to identify an ideal feature set for developing classification models. Experimental results show that an artificial neural network (ANN) classifier with five hand motion features selected by a correlation-based algorithm achieves the highest accuracy of 91.17% while an ANN with five force features has only 80.06%. This study corroborates that a simple assessment system based on a small number of hand motion features can be effective in assisting ETI training.

Preliminary Experience With Inertial Movement Technology to Characterize Endotracheal Intubation Kinematics.

BACKGROUND: Endotracheal intubation (ETI) is an important emergency intervention. Only limited data describe ETI skill acquisition and often use bulky technology, not easily transitioned to the clinical setting. In this study, we used small, portable inertial detection technology to characterize intubation kinematic differences between experienced and novice intubators. METHODS: We performed a prospective study including novice (<10 prior clinical ETI) and experienced (>100 clinical ETI) emergency providers. We tracked upper extremity motion with roll, pitch, and yaw using inertial measurement units (IMU) placed on the bilateral hands and wrists of the intubator. Subject performed 6 simulated emergency intubations on a mannequin. Using machine learning algorithms, we determined the motions that best discriminated experienced and novice providers. RESULTS: We included data on 12 novice and 5 experienced providers. Four machine learning algorithms (artificial neural network, support vector machine, decision tree, and K-nearest neighbor search) were applied. Artificial neural network had the greatest accuracy (95% confidence interval) for discriminating between novice and experienced providers (91.17%, 90.8%-91.5%) and was the most parsimonious of the tested algorithms. Using artificial neural network, information from 5 movement features (right hand, roll amplitude; right hand, pitch amplitude; right hand, yaw standard deviation; left hand, yaw standard deviation; left hand, pitch frequency of peak amplitude) was able discriminated experienced from novice providers. CONCLUSIONS: Novice and experienced providers have different ETI movement patterns and can be distinguished by 5 specific movements. Inertial detection technology can be used to characterize the kinematics of emergency airway management.