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ACTIVE NOT RECRUITING
NCT07566078
NA

Artificial Intelligence (AI)-Based Intraoperative Visualization is Increasingly Integrated Into Robotic Surgery Platforms; However, Its Impact on Surgeons' Cognitive Workload Remains Unclear. This Study Evaluated Perceived Workload Among Console Surgeons and Bedside Assistants According to Different

Sponsor: University of Illinois at Chicago

View on ClinicalTrials.gov

Summary

Robotic surgery is now widely adopted in urology, and the da Vinci Single-Port (SP) platform enables complex procedures through a single multichannel incision, with favorable perioperative and outpatient outcomes in selected patients. However, single-port access and AI implementation also introduce unique ergonomic and cognitive challenges for surgeons and operating room staff. Quantifying intraoperative workload has become crucial to understand how new technologies affect performance, safety and training. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) is a validated multidimensional instrument for subjective workload assessment and has been increasingly applied to surgical and specifically urologic practice. In parallel, augmented reality and artificial intelligence (AI) are emerging as tools to enhance intraoperative visualization and anatomical understanding during robot-assisted urologic procedures. The da Vinci TilePro multi-image display already allows simultaneous viewing of auxiliary imaging, but evidence on how real-time AI overlays integrated via TilePro affect cognitive workload in single-port urologic surgery is lacking. This prospective pilot study evaluates the impact of different TilePro visualization strategies on surgeon and bedside assistant workload, measured by weighted NASA-TLX scores, and explores associations with operative metrics in elective SP urologic procedures.

Official title: Impact of AI Visualization on Surgeons' Cognitive Workload in Single-Port Robotic Surgery

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

INTERVENTIONAL

Enrollment

90

Start Date

2025-09-01

Completion Date

2028-09-01

Last Updated

2026-05-04

Healthy Volunteers

Yes

Conditions

Interventions

OTHER

Yolo

The AI system employed in this study was based on a convolutional neural network (CNN) architecture implemented via the YOLO (You Only Look Once) framework, specifically designed for real-time instance segmentation of intraoperative anatomical structures.

Locations (1)

UI Health

Chicago, Illinois, United States