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.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.

OBJECTIVE: This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS). BACKGROUND: The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL. METHOD: The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL. RESULTS: The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA. CONCLUSION: A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies. APPLICATION: The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.

A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery.

Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon's dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant's tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.

Evaluation of an augmented reality platform for austere surgical telementoring: a randomized controlled crossover study in cricothyroidotomies.

Telementoring platforms can help transfer surgical expertise remotely. However, most telementoring platforms are not designed to assist in austere, pre-hospital settings. This paper evaluates the system for telementoring with augmented reality (STAR), a portable and self-contained telementoring platform based on an augmented reality head-mounted display (ARHMD). The system is designed to assist in austere scenarios: a stabilized first-person view of the operating field is sent to a remote expert, who creates surgical instructions that a local first responder wearing the ARHMD can visualize as three-dimensional models projected onto the patient's body. Our hypothesis evaluated whether remote guidance with STAR could lead to performing a surgical procedure better, as opposed to remote audio-only guidance. Remote expert surgeons guided first responders through training cricothyroidotomies in a simulated austere scenario, and on-site surgeons evaluated the participants using standardized evaluation tools. The evaluation comprehended completion time and technique performance of specific cricothyroidotomy steps. The analyses were also performed considering the participants' years of experience as first responders, and their experience performing cricothyroidotomies. A linear mixed model analysis showed that using STAR was associated with higher procedural and non-procedural scores, and overall better performance. Additionally, a binary logistic regression analysis showed that using STAR was associated to safer and more successful executions of cricothyroidotomies. This work demonstrates that remote mentors can use STAR to provide first responders with guidance and surgical knowledge, and represents a first step towards the adoption of ARHMDs to convey clinical expertise remotely in austere scenarios.

Evaluation of administered dose using portal images in craniospinal irradiation of pediatric patients.

This study aimed to assess the administered dose based on portal imaging in craniospinal pediatric irradiation by evaluating cases in which portal images did or did not account for the total administered dose. We also intended to calculate the mean increase in total administered dose. Data were collected from General University Hospital Gregorio Marañón; we evaluated the total dose administered, total dose planned, number of portal images per treatment and corresponding monitor units of two different groups: one in which the dose from portal images is deducted from the total administered dose (D), and another in which it was not (N). We used descriptive statistics to analyze the collected data, including the mean and respective standard deviation. We used the Shapiro-Wilk and Spearman rank correlation coefficient tests and estimated the linear regression coefficients. Patients in group D received a mean dose of 29.00 ± 10.28 cGy based on the verification portal images, a quantity that was deducted from the planned dose to match the total administered dose. Patients in group N received a mean dose of 41.50 ± 30.53 cGy, which was not deducted from the planned dose, evidencing a mean increase of 41.50 ± 30.55 cGy over the total administered dose. The acquisition of the set-up verification portal images, without their inclusion in the total administered dose, reflects an average increase in total dose for craniospinal irradiation of pediatric patients. Subtraction of the monitor units used to acquire the verification images is recommended.