Development of machine learning techniques for accessible and inexpensive imaging of COVID‑19 with ultrasound
Recent papers from Emergency Room (ER) researchers in Italy and China has shown several advantages of imaging the lungs with ultrasound instead of the current practice of using Computed Tomography (CT) or chest X-ray. These papers showed that COVID‑19 infection can be detected using B-lines, horizontal lines in the ultrasound image that are created because of the COVID‑19 infection.
Clarius ultrasound devices can be used as inexpensive screening tools in COVID‑19 test-pods, for pre-triage, and in bedside for monitoring patients. They can also be used for imaging pregnant women who cannot undergo CT or X-ray imaging due to radiation exposure. Finally, Clarius ultrasound devices are more than 200 times less expensive than CT scanners, which can substantially save healthcare costs.
Despite these attractive features, ultrasound remains user-dependent, and requires a skilled sonographer to collect high-quality images. In addition, detecting B-lines in ultrasound images entails careful inspection of images by an expert sonographer, which further limits accessibility of this screening tool. Given the current shortage of medical professionals at ERs, this partnership has two goals to address these issues:
1. Simplify image collection by automatically selecting the best ultrasound image as a novice user moves and rotates the probe on the chest wall.
2. Simplify interpretation of images by automatically detecting and localize B-lines in the best frame.
In close collaboration with Clarius, we will develop novel Neural Networks (NN) that address these problems. We will fabricate phantoms that mimic healthy and infected lungs to train and test our NN. We will exploit latest advances in machine learning to develop NNs that work for unseen data from different patients with different image settings.