| dc.description.abstract | Cricothyrotomy serves as one of the most efficient surgical interventions when a patient is enduring a can't intubate can't oxygenate (CICO) scenario. However, medical background and professional training are required for the provider to establish a patent airway successfully. Motivated by robotics applications in search and rescue, this work focuses on applying artificial intelligence techniques to the precise localization of the incision site, the cricothyroid membrane (CTM), of the injured using an RGB-D camera, and the manipulation of a robot arm with reinforcement learning to reach the detected CTM keypoint. In this paper, we proposed a deep learning-based model, the hybrid neural network (HNNet), to detect the CTM with a success rate of 96.6%, yielding an error of less than 5 mm in real-world coordinates. In addition, a separate neural network was trained to manipulate a robotic arm for reaching a waypoint with an error of less than 5 mm. An integrated system that combines both the perception and the control techniques was built and experimentally validated using a human-size manikin to prove the overall concept of autonomous cricothyrotomy with an RGB-D camera and a robotic manipulator using artificial intelligence. | |
