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

Final Pr. ID: Poster #: SCI-008

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

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