Selfie app detects pancreatic cancer by seeing whites of eyes
September 12, 2017
University of Washington researchers are developing an app that could allow people to screen for pancreatic cancer and other diseases by snapping a smartphone selfie.
Pancreatic cancer has one of the worst prognoses with a five-year survival rate of nine per cent, in part because there are no telltale symptoms or non-invasive screening tools to catch a tumour before it spreads.
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. The app is described in a paper to be presented this week at Ubicomp 2017, the Association for Computing Machinery’s international conference on pervasive and ubiquitous computing.
One of the earliest symptoms of pancreatic cancer, as well as other diseases, is jaundice, a yellow discoloration of the skin and eyes caused by a build-up of bilirubin in the blood. 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 programme for at-risk individuals.
In an initial clinical study of 70 people, the app used with a 3D printed box that controls the eye’s exposure to light correctly identified cases of concern 89.7 per cent of the time.
“The problem with pancreatic cancer is that by the time you’re symptomatic, it’s frequently too late,” said lead author Alex Mariakakis, a doctoral student at the Paul G Allen School of Computer Science & Engineering. “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.”
BiliScreen builds on earlier work from the UW’s Ubiquitous Computing Lab, which previously developed BiliCam, a smartphone app that screens for newborn jaundice by taking a picture of a baby’s skin. A recent study in the journal Pediatrics showed BiliCam provided accurate estimates of bilirubin levels in 530 infants.
In collaboration with UW Medicine doctors, the UbiComp lab specialises in using cameras, microphones and other components of common consumer devices such as smartphones and tablets to screen for disease.
The blood test that doctors currently use to measure bilirubin levels – which is typically not administered to adults unless there is reason for concern – requires access to a health care professional and is inconvenient for frequent screening. 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, BiliScreen could also potentially ease the burden on patients with pancreatic cancer who require frequent bilirubin monitoring.
In adults, the whites of the eyes are more sensitive than skin to changes in bilirubin levels, which can be an early warning sign for pancreatic cancer, hepatitis or the generally harmless Gilbert’s syndrome. Unlike skin colour, changes in the sclera are more consistent across all races and ethnicities.
Yet by the time people notice the yellowish discoloration in the sclera, bilirubin levels are already well past cause for concern. The UW team wondered if computer vision and machine learning tools could detect those colour changes in the eye before humans can see them.
“The eyes are a really interesting gateway into the body,” said senior author Shwetak Patel, a professor at the university. “Tears can tell you how much glucose you have, sclera can tell you how much bilirubin is in your blood. Our question was: Could we capture some of these changes that might lead to earlier detection with a selfie?”
BiliScreen uses a smartphone’s built-in camera and flash to collect pictures of a person’s eye as they snap a selfie. The team developed a computer vision system to isolate automatically and effectively the white parts of the eye, which is a valuable tool for medical diagnostics. The app then calculates the colour 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.
To account for different lighting conditions, the team tested BiliScreen with two different accessories: paper glasses printed with coloured squares to help calibrate colour and a 3D printed box that blocks out ambient lighting. Using the app with the box accessory – reminiscent of a Google cardboard headset – led to slightly better results.
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 such as the box and glasses.
“This relatively small initial study shows the technology has promise,” said co-author Jim Taylor, a professor at the university whose father died of pancreatic cancer at age 70. “Pancreatic cancer is a terrible disease with no effective screening right now. 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.”
Co-authors include Allen School undergraduate student Megan Banks, research study coordinator Lauren Phillipi and assistant professor of medicine Lei Yu. The research was funded by the National Science Foundation, the Coulter Foundation and endowment funds from the Washington Research Foundation.