Deep-learning algorithm diagnoses pneumonia in 10 seconds
If a newly tested AI system for reading chest X-rays achieves widespread adoption, patients presenting in the ER with symptoms of pneumonia can expect an up or down diagnosis—and with it the start of a treatment plan—in 10 seconds.
Till now, the norm has been 20-plus minutes, as it can take that long for a busy emergency radiologist to get around to reading the exam.
The deep-learning system, called CheXpert, was developed by researchers at Stanford University and implemented at Utah-based Intermountain Healthcare. It was introduced Monday in Madrid, Spain, at an international gathering of the European Respiratory Society, according to an Intermountain news release.
Stanford’s machine learning group initially used 188,000 chest X-rays from California to train the algorithm on what is and isn’t pneumonia. Intermountain supplied an additional 7,000 or so images to fine-tune the model for its patient population.
Testing the system on 461 patients in Utah, researchers from both institutions found its 10-second detection of critical findings had high agreement with that of three experienced radiologists.
“CheXpert is going to be faster and as accurate as radiologists viewing the studies,” says Nathan Dean, MD, in prepared remarks. Dean is principal investigator of the study and section chief of pulmonary and critical care medicine at Intermountain Medical Center in Salt Lake City.
Dean expects the model to go live in several Intermountain ERs this fall.
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.