An Indian American-led research group at Stanford University has developed an algorithm that offers diagnoses based off chest X-ray images.
The algorithm, developed by a team led by Stanford Machine Learning Group graduate student Pranav Rajpurkar, can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia better than expert radiologists working alone, according to a university report.
“Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there’s a lot of variability in the diagnoses radiologists arrive at,” said Rajpurkar in the report. “We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses.”
The work uses a public dataset initially released by the National Institutes of Health Clinical Center on Sept. 26, the report said.
That dataset contains 112,120 frontal-view chest X-ray images labeled with up to 14 possible pathologies. It was released in tandem with an algorithm that could diagnose many of those 14 pathologies with some success, designed to encourage others to advance that work, according to the university report.
The researchers, working with Matthew Lungren, an assistant professor of radiology, had four Stanford radiologists independently annotate 420 of the images for possible indications of pneumonia, it said.
The researchers have chosen to focus on this disease, which brings 1 million Americans to the hospital each year, according to the Centers for Disease Control and Prevention, and is especially difficult to spot on X-rays, the researchers said.
In the meantime, the Machine Learning Group team got to work developing an algorithm that could automatically diagnose the pathologies, the report added.
The researchers had an algorithm within a week that diagnosed 10 of the pathologies labeled in the X-rays more accurately than previous state-of-the-art results.
In just over a month, their algorithm could beat these standards in all 14 identification tasks, the report said.
After about a month of continuous iteration, the algorithm outperformed the four individual Stanford radiologists in pneumonia diagnoses, it said, adding that the algorithm now has the highest performance of any work that has come out so far related to the NIH chest X-ray dataset.