Researchers at the University of Washington have developed an app that could allow people to screen for pancreatic cancer by using a smartphone selfie.

Pancreatic cancer patients have a five-year survival rate of just 9 percent, one of the worst prognoses for any type of cancer due to the lack of symptoms and non-invasive screening tools. One of the earliest symptoms of pancreatic cancer and other diseases is jaundice, a yellowing of the skin and eyes caused by an increase in bilirubin in the blood.

The blood test currently used to screen for bilirubin levels is not given to adults unless there is a cause for concern, requires access to a health care professional and is inconvenient for frequent screening.

The app called, BiliScreen, uses a smartphone camera, computer vision algorithms and machine learning tools to detect increased bilirubin levels in a person's sclera, the white part of the eye.

"The problem with pancreatic cancer is that by the time you're symptomatic, it's frequently too late," Alex Mariakakis, a doctoral student at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, said in a press release. "The hope is that if people can do this simple test once a month -- in the privacy of their own homes -- some might catch the disease early enough to undergo treatment that could save their lives."

The study, which will be presented Sept. 13 at Ubicomp 2017, the Association for Computing Machinery's International Joint Conference on Pervasive and Upiquitous Computing, consisted of 70 people using the BiliScreen app, in conjunction with a 3D printed box that controls the eye's exposure to light. The test correctly identified cases 89.7 percent of the time, which is significantly higher than the blood test currently used.

"The eyes are a really interesting gateway into the body -- tears can tell you how much glucose you have, sclera can tell you how much bilirubin is in your blood," said Shwetak Patel, the Washington Research Foundation Entrepreneurship Endowed Professor in Computer Science & Engineering and Electrical Engineering. "Our question was: Could we capture some of these changes that might lead to earlier detection with a selfie?"

The app calculates the color information from the sclera and correlates it with bilirubin levels using machine learning algorithms.

The research expands on earlier work from the lab, which developed the BiliCam, a smartphone app that screens for newborn jaundice by taking a picture of a baby's skin.