Wearable ultrasound with machine learning for automated assessment of pneumothorax

The proposed research project will develop and validate a novel automated system that can continuously monitor for PTX (collapsed lungs). This system will integrate inexpensive, flexible, thin, and wearable ultrasonic sensors with machine learning. A hands-free and automated approach to assessing for PTX allows clinicians, or even personnel with less specialized training, to detect PTX earlier, make timely medical interventions, and manage multiple patients simultaneously. This project will help Deep Breathe Inc. develop and advance a new category of hands-free ultrasound technology that has the potential to revolutionize respiratory care, reduce the strain on healthcare resources, and improve the accessibility of lung ultrasound.

Faculty Supervisor:

Yuu Ono

Student:

Partner:

Deep Breathe

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Carleton University

Program: