This research aims to improve anatomical recognition during robotic surgeries to support novice surgeons.
Developed a deep-learning segmentation model for anatomical structures.
Evaluated the model's performance in recognizing the bladder neck during surgery.
Assessed effects on speed and accuracy for novice surgeons.
The segmentation model significantly improved the recognition speed of anatomical structures.
Performance enhancements were particularly beneficial for novice surgeons.
Potential implications for better training in surgical education.
Abstract
The developed segmentation model enhanced the speed and performance of anatomical recognition, particularly for novice surgeons, and may support surgical education.