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

Abstract

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.

The Use of Integrated Multichannel Records in Learning Studies in Higher Education: A Systematic Review of the Last 10 Years

“…Asia is the continent with the largest proportion of publications, with five studies published in China [4,46,49,51,53] and one in Turkey [50], representing 60% of the articles selected in the sample. The secondhighest number of publications was in the USA (20%) [47,52]. Finally, two articles in the last 10 years were published in Europe (20%): one in Spain in 2023 [3] and another in Denmark in 2019 [49].…”

“…The largest sample in the studies was 78 subjects [49], while the smallest sample had 10 participants [47]. The total number of participants in the selected studies was 416 subjects (M = 41.6, SD = 24.4).…”

“…The total number of participants in the selected studies was 416 subjects (M = 41.6, SD = 24.4). The percentage of women in the sample ranged between 20% [47] and 80% [52]. However, half of the studies [3,48,50,51,53] had samples with 50% women.…”

Clinical applications of artificial intelligence in robotic surgery

“…Wu et al investigated how live data acquired during any robotic endoscopic surgical procedure may be used to correct inaccurate FEM simulation results using an open-source da Vinci Surgical System to probe a soft tissue phantom and replay the interaction in simulation. They trained a neural network to correct for the difference between the predicted mesh position and the measured point cloud and showed improved FEM results by 15–30% over various simulation parameters [ 35 ].…”

Cognitive ergonomics and robotic surgery

“…Multimodal sensing outperformed individual sensors for predicting cognitive load. Lim et al demonstrated that EEG spectral analysis could distinguish the cognitive workload of a surgeon not only relating to surgical task difficulty but also to multi-tasking requirement [ 42 ].…”