“We’ve been developing a deep learning algorithm that can automatically detect pneumonia and related findings in chest X-rays,” said Jeremy Irvin, a PhD student at Stanford, and member of the research team. “In this initial study, we’ve demonstrated the algorithm’s potential by validating it on patients in the emergency departments at Intermountain Healthcare. Our hope is that the algorithm can improve the quality of pneumonia care at Intermountain, from improving diagnostic accuracy to reducing time to diagnosis.”
Developed by Intermountain Healthcare and Stanford University, the developed AI system will help to faster and more accurately detect the presence of Pneumonia within a short time span of about 10 seconds; doing this will aid with developing a faster means of treating patients, especially the severely ill patients.
“CheXpert is going to be faster and as accurate as radiologists viewing the studies. It’s an exciting new way of thinking about diagnosing and treating patients to provide the very best care possible,” said Nathan C. Dean, MD, principal investigator of the study, and section chief of pulmonary and critical care medicine at Intermountain Medical Center in Salt Lake City. For most emergency departments where ePNa is not available, the CheXpert model could provide the information from chest X-rays directly to clinicians. “Using the CheXpert system, we found the interpretation time was very swift and the accuracy of the report to be very high,” he added.
Findings from the collaborative study were presented at the European Respiratory Society’s International Congress 2019, held in Madrid, Spain, this week.
“A 2013 study published in JAMA Internal Medicine found that 59 percent of errors made by ePNA were due to NLP processing of radiologist reports, so we’re eager to replace it with a better, faster system,” Dr. Dean said.