Point of Care Lung ultrasound has proven in multiple studies to be superior to CXR to diagnose pneumonia in children especially in limited resource settings. This non-radiating, portable and adaptable technique, brings an opportunity to detect pneumonia with higher accuracy than CXR. Ultrasound imaging interpretation is challenging. To deal with this complexity, we created a "brightness profiles" data reduction technique to identify specific anatomical structures identified by lung ultrasound using artificial intelligence. We use this technique to demonstrate how data reduction can help identify common anatomical landmarks and abnormal findings, and aid in the interpretation of ultrasound diagnosed pediatric pneumonia. Read More

Meeting name: SPR 2022 Annual Meeting & Postgraduate Course , 2022

Authors: Li Jason, Betke Margaret, Gill Christopher, Thompson Russell, Wang Kaihong, Etter Lauren, Camelo Ingrid, Chang Hailey, Setty Bindu, Castro Ilse, Pieciak Rachel

Keywords: Artificial intelligence, pneumonia, ultrasound

CXR is the most common imaging method to diagnose pneumonia in children in limited-resource settings. There is a need to simplify and expedite its interpretation. By using a machine learning model to first classify and interpret the pneumonia images and then incorporate those characteristic imaging findings patterns into a simulated mobile app, health care workers can use their mobile devices to interpret those findings based on preloaded images built into their mobile devices corresponding to pneumonia. Read More

Meeting name: SPR 2022 Annual Meeting & Postgraduate Course , 2022

Authors: Thompson Russell, Pieciak Rachel, Gill Christopher, Li Jason, Wang Kaihong, Etter Lauren, Camelo Ingrid, Castro-aragon Ilse, Setty Bindu, Chang Hailey, Betke Margaret

Keywords: Artificial intelligence, pneumonia, CXR