I-Corps: Translation Potential of an Automated Tracheotomy Assistive Device

Award ID: 2444592

The broader impact of this I-Corps project is based on the development of an Artificial Intelligence (AI)-guided tracheotomy assist device that enhances the safety, speed, and accessibility of emergency airway procedures. By combining ultrasound technology with advanced machine learning algorithms, the device assists providers in accurately locating critical anatomic landmarks for tracheotomy and cricothyrotomy procedures. This system allows non-expert personnel to perform life-saving airway access in settings where trained specialists may not be available, such as during emergencies in the field or in remote locations. The device's potential to reduce complications and improve success rates could transform emergency response practices across healthcare settings, including emergency rooms, ambulances, military medical units, and intensive care units (ICUs). In the long term, this technology could become common in public spaces, giving first responders and civilians a reliable, intuitive tool to manage severe airway obstructions and save lives. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a portable device that integrates an ultrasound probe with a machine-learning algorithm to guide airway cannulation. The device detects and marks anatomical landmarks in real time, using Artificial Intelligence (AI) to set the ideal cannulation angle and depth while avoiding critical structures. Research has demonstrated that the device can successfully distinguish airway structures and provide precise guidance for accessing the trachea in emergency scenarios. Unlike existing tools, which primarily aid incision control, this innovation actively identifies and navigates complex anatomy, addressing a key reason for procedural failure. This approach provides a novel solution that has the potential to empower more healthcare providers to perform this life-saving intervention with confidence and accuracy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman