Automated Non-Invasive Method Of Diagnosing Eye Surface Cancer
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Automated Non-Invasive Method Of Diagnosing Eye Surface Cancer

Australian Researchers have developed an automated, non-invasive technique for diagnosing eye surface cancer, which may lower the need for biopsies, preventing therapy delays and is a far more effective method.

The result is an automated system that can successfully differentiate between diseased and non-diseased eye tissue, in real time, through a very simple scanning procedure.

“The first detection of OSSN is critical as it supports easy and much more curative treatments such as topical remedies whereas advanced lesions may require eye surgery as well as the removal of the eye, and also has the danger of mortality,” said Habibalahi, lead scientist on the project, who works at Australian Research Council (ARC) Centre of Excellence.

Researchers have developed is a technological approach that uses the power of the two microscopy and cutting-edge machine learning.

“Our hi-tech system scans the natural light given off by specific cells of the eye, after being stimulated by secure levels of artificial light,” explained Habibalahi.

“Diseased cells possess their own unique’light-wave’ signature which is specially designed computational algorithm is subsequently able to spot providing a speedy and efficient analysis,” he said.

Tissue samples from eighteen patients using OSSN were

tested.

“We identified that the diseased cells in all eighteen cases. A fast test using our automated system is all that is essential to pick up early warning signs of OSSN,” explained Habibalahi.

A key advantage of the innovative setup is the OSSN diagnosis foregoes the need to get a biopsy done.

“This benefits both the patient and the physician. Biopsies of the eye could be traumatic and may also be expensive and time intensive with samples needing to be sent to a lab for testing,” Habibalahi explained.

In addition to this early detection and non-invasive advantages, the technology can precisely map the position of abnormal tissue borders on the eye.

“Next steps would be to create our system functional and achievable in a clinical setting. We hope to do this by integrating our method into a standard retinal camera setup — like that used by opticians and optometrists when undertaking routine eye exams,” he explained.

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