Autonomous Cricothyroid Membrane Detection and Manipulation Using Neural Networks and a Robot Arm for First-Aid Airway Management


 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 on 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 further improved the success rate of our previously proposed Hybrid Neural Network (HNNet) in detecting the CTM from 84.3% to 96.6%, yielding an error of less than 5mm 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 5mm. An integrated system that combines both the perception and the control techniques was built and experimentally validated using a human-size manikin to validate the overall concept of autonomous cricothyrotomy with an RGB-D camera and a robotic manipulator using artificial intelligence.