TY - GEN
T1 - IC-RCM
T2 - 10th International Conference on Electrical Engineering, Control and Robotics, EECR 2024
AU - Alikhani, Alireza
AU - Fareghzadeh, Nida
AU - Inagaki, Satoshi
AU - Maier, Mathias
AU - Navab, Nassir
AU - Nasseri, M. Ali
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Robot-Assisted ophthalmic surgery (RAOS) advancements have transformed precision in eye surgical procedures. RAOS offers unparalleled accuracy and dexterity, allowing for intricate maneuvers in delicate ocular structures with minimized invasiveness. However, ensuring the reliability and quality of the instruments' pivot point at the trocar, called Remote Center Motion (RCM), is imperative to uphold patient safety and surgical efficacy. Hence, real-Time monitoring and tracing of RCM performance parameters are pivotal in minimizing surgical trauma and averting adverse outcomes. Our approach showcases a precise pipeline for recognizing RCM misalignment by integrating the robot's kinematic software data with neural network-based pose estimation methodologies with the mean absolute rotation error of 5.48, tested on a 23G surgical cannula with a diameter of 0.640 mm. This method uses a high-resolution camera at 4024 × 3036 capable of processing at 20 frames per second.
AB - Robot-Assisted ophthalmic surgery (RAOS) advancements have transformed precision in eye surgical procedures. RAOS offers unparalleled accuracy and dexterity, allowing for intricate maneuvers in delicate ocular structures with minimized invasiveness. However, ensuring the reliability and quality of the instruments' pivot point at the trocar, called Remote Center Motion (RCM), is imperative to uphold patient safety and surgical efficacy. Hence, real-Time monitoring and tracing of RCM performance parameters are pivotal in minimizing surgical trauma and averting adverse outcomes. Our approach showcases a precise pipeline for recognizing RCM misalignment by integrating the robot's kinematic software data with neural network-based pose estimation methodologies with the mean absolute rotation error of 5.48, tested on a 23G surgical cannula with a diameter of 0.640 mm. This method uses a high-resolution camera at 4024 × 3036 capable of processing at 20 frames per second.
KW - AI for Medical Robotics
KW - Medical Robots and Systems
KW - Robot Safety
KW - Sensor-Based Navigation
UR - http://www.scopus.com/inward/record.url?scp=85202450473&partnerID=8YFLogxK
U2 - 10.1109/EECR60807.2024.10607316
DO - 10.1109/EECR60807.2024.10607316
M3 - Conference contribution
AN - SCOPUS:85202450473
T3 - 2024 10th International Conference on Electrical Engineering, Control and Robotics, EECR 2024
SP - 111
EP - 117
BT - 2024 10th International Conference on Electrical Engineering, Control and Robotics, EECR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 March 2024 through 31 March 2024
ER -