Using artificial intelligence to classify point-of-care ultrasound images

Modern point of care ultrasound (POCUS) devices can perform echocardiograms from a smartphone, greatly improving accessibility. However, operator expertise is still required to gather high quality data which is needed to accurately view and diagnose patients. The goal of this study is to enhance the collection of mobile ultrasound echocardiograms with AI machine learning. AI can provide feedback to a POCUS operator to help maximize clinical usability of data. To realize this, we used the Intel GETi framework to create computer vision models that quantify the readability of frames taken from an echocardiogram. These models determine the quality and the orientation of each frame. Feedback from these models can alert the user to proper positioning and technique to gather usable ultrasound data. Tests on existing data show the accuracy of the models ranging from 77%-99%. As the GETi framework develops further, it has the potential to perform these tests in real time from a mobile device.