The app could potentially help hundreds of thousands of people with the deadly cancer get much-needed treatment during the early stages of the disease. Early detection can improve their chances of surviving this very aggressive form of cancer which is estimated to have caused the death of 411,600 people in 2015.
One of the symptoms of pancreatic cancer is jaundice.
“Jaundice is only recognizable to the naked eye in severe stages, but a ubiquitous test using computer vision and machine learning can detect milder forms of jaundice,” according to a study by researchers Alex Mariakakis, Megan A. Banks, Lauren Phillipi, Lei Yu, James Taylor, and Shwetak Patel. “We propose BiliScreen, a smartphone app that captures pictures of the eye and produces an estimate of a person’s bilirubin level, even at levels normally undetectable by the human eye.”
BiliScreen is a smartphone camera app which uses machine learning to estimate the extent of jaundice in the sclera – the white outer layer of the eyeball.
The researchers focused on measuring jaundice in the eye since the typical sclera is race-agnostic, unlike skin which widely differs across ethnicities.
Jaundice in the skin and eyes are caused by a buildup of bilirubin in the blood. Bilirubin is a substance found in bile. It is produced when the liver breaks down old red blood cells.
Jaundice is not apparent to the trained naked eye until roughly 3.0 mg/dl, according to the researchers. However, bilirubin levels greater than 1.3 mg/dl “warrant clinical concern.”
“There exists a detection gap between 1.3 and 3.0 mg/dl that is missed by clinicians unless a TSB (a total serum bilirubin) is requested, which is rarely done without due cause,” according to the study. “We hypothesize that diagnoses can be made much earlier and lead to better outcomes with a system that is precise enough to distinguish between bilirubin levels within and outside of those bounds.”
The researchers said the BiliScreen app is able to snap pictures of the eye and produce an estimate of a person’s bilirubin level at levels that are not “normally undetectable by the human eye.”
In a clinical study of 70 people, the researchers found that BiliScreen achieves a person correlation coefficient of 0.89 and a mean error of -0.09 ± 2.76 mg/dl in predicting a person’s bilirubin level.BiliScreen identifies cases of concern with a sensitivity of 89.7 per cent and a specificity of 96.8 per cent with the box accessory, according to the researchers.
Because different ambient lighting can change the colour of the same scene, the researchers are evaluating two accessories for ambient lighting conditions.
One of the accessories is a head-worn box similar to commercially available virtual reality headpieces that attach to smartphones. The box is designed to block out ambient light. It uses the phone’s camera flash to control lighting.
The other accessory is a pair of paper glasses similar to the 3D glasses worn by movie watchers.
Since the sclera does not have a predefined shape, BiliScreen also requires an additional step of segmentation.
The team hopes to continue building improved models of BiliScreen and conduct a longer-term study which will capture trends of bilirubin levels.
The study was funded by the National Science Foundation and the Coulter Foundation.
The paper will be presented on September 13 at the Ubicomp 2017 which is the Association for Computing Machinery’s International Joint Conference on Pervasive and Ubiquitous Computing.
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