Machine Vision-Based Surgical Lighting System:Design and Implementation
This addresses surgeon strain and lighting inconsistency in surgical procedures, representing an incremental improvement over traditional manual systems.
The paper tackles the problem of manual surgical lighting adjustments causing surgeon fatigue and inconsistent illumination by proposing a machine vision-based system that uses YOLOv11 to detect a blue marker and automatically direct lighting, achieving 96.7% mAP@50 on validation data.
Effortless and ergonomically designed surgical lighting is critical for precision and safety during procedures. However, traditional systems often rely on manual adjustments, leading to surgeon fatigue, neck strain, and inconsistent illumination due to drift and shadowing. To address these challenges, we propose a novel surgical lighting system that leverages the YOLOv11 object detection algorithm to identify a blue marker placed above the target surgical site. A high-power LED light source is then directed to the identified location using two servomotors equipped with tilt-pan brackets. The YOLO model achieves 96.7% mAP@50 on the validation set consisting of annotated images simulating surgical scenes with the blue spherical marker. By automating the lighting process, this machine vision-based solution reduces physical strain on surgeons, improves consistency in illumination, and supports improved surgical outcomes.