The Australian Research Council’s Centre for Nanoscale BioPhotonics (CNBP) has developed an automated system to detect ocular surface squamous neoplasia (OSSN).
This non-invasive technique combines advanced imaging microscopy with computing and artificial intelligence (AI) to differentiate diseased and non-diseased eye tissue in real-time.
Go deeper with GlobalData
It scans the natural light reflected by certain eye cells following stimulation with safe levels of artificial light.
According to the researchers, the light-wave signature of diseased cells is unique and can be identified by a computational algorithm.
CNBP researcher Habibalahi said: “Clinical symptoms of OSSN are known to be variable and in early stages can be extremely hard to detect so patients may experience delays in treatment or be inaccurately diagnosed.
“The early detection of OSSN is critical as it supports simple and more curative treatments such as topical therapies whereas advanced lesions may require eye surgery or even the removal of the eye, and also has the risk of mortality.”
How well do you really know your competitors?
Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.
Thank you!
Your download email will arrive shortly
Not ready to buy yet? Download a free sample
We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below form
By GlobalDataWhen tested using tissue samples from 18 OSSN patients, the automated system was able to detect the diseased cells in all cases.
The CNBP research team said that the system eliminates the need for biopsy, which could be invasive, expensive and time-consuming.
Habibalahi noted: “We will be able to confirm the disease straight away through a simple eye scan with no biopsy required and appropriate action can be quickly progressed by the specialist.”
The AI-based system can precisely map the location of abnormal tissue margins, enabling more accurate and effective treatment. The researchers believe the technology can be applied in an outpatient setting by integrating into a standard retinal camera.
