URMC / Health Lab / Projects / Butterfly

POCUS | URMC | Butterfly Network

Through a partnership with Butterfly Network, Inc. and other industry partners, URMC aims to tranform care with next generation point-of-care ultrasound (POCUS) devices. This partnership is an enterprise-wide initiative that touches all aspects of URMC's mission. Clincal areas of focus include education and curriculum for point-of-care ultrasounds, the creation of encounter-based workflows, development of best practices for system deployment, infrastructure integration, quality assurance, and three-dimensional procedural guidance.

Butterfly probe

Overview

Within the Health Lab, multiple projects use POCUS with the goal to transform healthcare delivery and access via machine learning and AI-based technologies. We seek to develop new biomarkers from medical images, physiological sensors, and patient records. This combination of tech and advancement helps URMC with clinical decision support and streamlines clinical workflow, all increasing access to top-notch healthcare.

Team

Those working on these projects include Ajay Anand, Jeff Lillie, Lisa Pink, and various students from computer and data science backgrounds. On the clinical side, the team relies on the knowledge of Dave Mitten, Constantinos Ketonis, Andrew Rodenhouse, and Spencer Rosero.

Health Lab team members demonstrating how to use the Butterfly probe

Projects

In addition to the strong relationships with medical imaging providers, like Butterfly, various projects are underway with the goal of developing end-to-end data acquisition and analysis tools to integrate into existing workflows. Current research and implementation methods include deep learning, machine learning, statistical analysis, and signal and image processing. These methods are facilitated by various bioinstruments and handheld ultrasound hardware/software. Projects include:

Osteoporosis Detection using X-Rays
Early Cubital and Carpal Tunnel Syndrome Detection using Ultrasound Imaging
3D Tracking of Physiological Movements | Additive measures from EMG sensors help quantify risk and track improvements.
Noninvasive Cardiac Output and Aortic Pressure Measurement | This project measures peripheral flow and pressure measurements using ultrasound and other physiological sensors at the wrist, along with AI-enhanced first principal physics-based models.