Friday, 8 September 2017

Eye Scans to Detect Cancer and Alzheimer’s Disease

By Ask Biomedical

Eye Scans to Detect Cancer and Alzheimer’s Disease-

It is said that eyes are the window to the soul. Well, what about the window to our health?

Two AI systems trained to detect eye diseases, specifically diabetic retinopathy and congenital cataracts.
 There has been found groups extending that concept to illnesses beyond the eye. Two new projects pair imaging systems with advanced software in an effort to catch early symptoms of pancreatic cancer and Alzheimer’s disease.

Researchers have created a smartphone app to screen for pancreatic cancer with a quick selfie. They have recently tested their system in a clinical study of 70 people. They were able to identify cases of concern with 89.7 percent sensitivity and 96.8 percent accuracy.

While the app is not yet ready to be used as a diagnostic tool based only a single small trial, it might soon provide a tool for doctors monitor disease progression in patients undergoing treatment with a simple photograph, rather than a blood test !

The app, called BiliScreen, monitors levels of bilirubin, a metabolic byproduct that builds up in cases of jaundice, causing a yellowing of the skin and whites of the eyes.

 Increased bilirubin in the blood is one of the earliest symptoms of pancreatic cancer, but it is currently detected by a blood test in a doctor’s office and done only for patients at high-risk or with other symptoms.

The BiliScreen app detects levels of bilirubin in the whites of the eyes.

To create an easy-to-use, non-invasive early screen for pancreatic cancer—the researchers designed a three-step system.

.. First, users take a selfie with their smartphone using one of two accessories to control for environmental conditions: either a cardboard box to block out the light, or colored glasses to give a color reference. In the study, the team found that the box was slightly better at controlling light conditions than the glasses.

Once an image is captured, the software relies on computer vision algorithms to isolate the sclera, or white of the eye, from the skin and pupil.
The clinical trial version of the software was based on an algorithm from Microsoft Research called GrabCut. A newer iteration, which works even better to isolate the sclera, uses a fully convolutional neural network to identify the most important local features in each image. In that one can give the algorithm enough cases of labeled images with people of different skin tones, eye colors, and orientation, and it can figure out where the sclera is.

..Next, that image information is matched to levels of bilirubin taken from a blood draw and fed into a machine-learning algorithm to train it to detect images of concern. In the initial 70-person study, roughly half the participants had elevated levels of bilirubin and half did not. BiliScreen was able to detect those individuals with high levels with good accuracy and sensitivity, but both measures could be improved with more data.

Based on same concept , researchers have developed a sophisticated camera and retinal imaging appraoch to detect early signs of Alzheimer’s disease (AD). This system, recently detailed in a proof-of-concept trial published in the journal JCI Insight, relies on a specialized ophthalmic camera that is not yet available on a smartphone.

Researchers believe that the imaging could someday be adapted to less expensive cameras, but currently a doctor’s office would require a high-definition camera and image processing and quantification tools in order to use the system.

1.University of Washington (A team led by computer scientist Shwetak Patel)
2. Cedars-Sinai NeuroVision Imaging LLC, California


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