๐ In this paper, we unveil an innovative autonomous palpation-based acquisition strategy – RASEC, designed for the tracheal region. RASEC predicts the next acquisition point interactively, maximizing expected information and minimizing palpation procedure costs. By leveraging a Gaussian Process (GP) to model tissue hardness distribution and anatomical information as a guiding input for medical robots, RASEC revolutionizes robot-assisted subtasks in tracheotomy.
๐ก We introduce a dynamic tactile sensor based on resonant frequency to measure tissue hardness at millimeter-scale precision, ensuring secure interactions. By exploring kernel fusion techniques blending Squared Exponential (SE) and Ornstein-Uhlenbeck (OU) kernels, and optimizing Bayesian search with larynx anatomical data, we enhance exploration efficiency and accuracy.
๐ฌ Our research considers new factors like tactile sensor movement and robotic base rotation in the acquisition strategy. Simulation and physical phantom experiments demonstrate a remarkable 53.1% reduction in sensor movement and 75.2% reduction in base rotation, with superior algorithmic performance metrics (average precision 0.932, average recall 0.973, average F1 score 0.952) and minimal distance errors (0.423 mm) at a high resolution of 1 mm.
๐ The results showcase RASEC’s excellence in exploration efficiency, cost-effectiveness, and incision localization accuracy in real robot-assisted tracheotomy procedures.
This collaborative work is achieved by WENCHAO YUE, Fan Bai, Jianbang Liu, and Prof Hongliang Ren from The Chinese University of Hong Kong, Prof Feng Ju from Nanjing University of Aeronautics and Astronautics, Prof Max Q.-H. Meng from Southern University of Science and Technology, and Dr. Chwee Ming Lim from Singapore General Hospital.
Paper is available at https://lnkd.in/gEgmaDVj
