App that could use smartphone selfies to screen for pancreatic cancer

BiliScreen is designed to be an easy to use, non invasive tool that could help determine whether someone ought to consult a doctor for further testing. Beyond diagnosis, this application could also potentially ease the burden on patients with pancreatic cancer who require frequent bilirubin monitoring.

Dr Jim Taylor, is a professor in the UW Medicine Department of Pediatrics and is one of the team members whose father died of pancreatic cancer at age 70.

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

“Our goal is to have more people who are unfortunate enough to get pancreatic cancer to be fortunate enough to catch it in time to have surgery that gives them a better chance of survival”, said co-author Dr. Jim Taylor.
The team developed a computer vision system to automatically and effectively isolate the white parts of the eye, which is a valuable tool for medical diagnostics. The app then calculates the color information from the sclera, based on the wavelengths of light that are being reflected and absorbed, and correlates it with bilirubin levels using machine learning algorithms.

The ability to detect signs of jaundice when bilirubin levels are minimally elevated (but before they’re visible to the naked eye), could enable an entirely new screening program for at-risk individuals.
In an initial clinical study of 70 people, the BiliScreen app used in conjunction with a 3D printed box that controls the eye’s exposure to light, correctly identified cases of concern 89.7% of the time, compared to the blood test currently used.
“The problem with pancreatic cancer is that by the time you’re symptomatic, it’s frequently too late, (...) The hope is that if people can do this simple test once a month some might catch the disease early enough to undergo treatment that could save their lives.”, said lead author Alex Mariakakis, a doctoral student at the Paul G. Allen School of Computer Science & Engineering.

Next steps for the research team include testing the app on a wider range of people at risk for jaundice and underlying conditions, as well as continuing to make usability improvements, including removing the need for accessories like the box and glasses.

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