Google Glass: New Medical Diagnostics App Using Image Recognition

By March 26, 2014
google glass & diagnostic

A newly-developed Google Glass app enables you to take photos of rapid diagnostic test strips, have them remotely analysed and receive an instantaneous medical diagnosis.

Google Glass is proving its ability to work in tandem with the health sector. Google’s smart specs are about to be accepted as a medical device in their own right, and they have the potential to support a wide range of innovations in this field. For example researchers at the School of Engineering and Applied Science at UCLA (University of California Los Angeles) recently set out to harness Google Glass functionality to streamline certain kinds of basic medical diagnosis. Aydogan Ozcan, Professor of Electrical Engineering and Bioengineering at UCLA and the research team he heads up have now developed a dedicated application which enables a Google Glass wearer to obtain fast analysis by taking photos of rapid diagnostic test (RDT) assays and image comparison.

A medical innovation

The rapid diagnostics tests already used by doctors are a low-cost means of obtaining an analysis using test strips which change colour when they detect chemicals associated with the symptoms of a specific illness. With the new Google Glass app, users will be able to take a photo of these RDT strips and upload the image to a UCLA-designed server platform, which holds an image bank of medical analysis results. The system will automatically compare the photos that have been transmitted and provide a full diagnosis in less than eight seconds, say the team. The technology enables quantified reading of the results to a level of sensitivity far greater than that of the naked eye, thus eliminating the potential for human error in interpreting results, which is a particular concern if the user is a health care worker who routinely has to read and interpret many different types of tests.

Mobile health advancing

Aydogan Ozcan explains that “this smart app allows real-time tracking of health conditions and could be quite valuable in epidemiology, mobile health and telemedicine.” The technology has been tested with an in-home HIV test and a prostate-specific antigen test. The researchers took images of tests under normal, indoor, and fluorescent-lit room conditions. They submitted more than 400 images of the two tests, and the RDT reader and server platform were able to read the images 99.6% of the time, returning accurate, quantified test results. This appears to be yet another example of how mobile technology can contribute to improved healthcare.

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