One of the certainties I have is that shortly, perhaps very shortly, software and algorithms will totally replace humans in the diagnosis of diabetic retinopathy.

My opinion is based on the studies I read on this subject for a few years. These advances show us that technology and its applications they continue to improve, they are increasingly reliable, lower costs and increase the percentage of the population that can benefit from them. Obviously, you can disagree and if this is the case, I invite you to leave a comment at the end of the post since your opinion interests me. If you agree you can also comment, it would be missing more.

Returning to this new algorithm for diagnosing retinopathy, the researchers have perfected a new technology based on artificial intelligence (AI) that can detect a key marker of diabetic retinopathy and they have published it in a study that has recently appeared in Computers in Biology and Medicine (the link at the end of the post).

The team of Brazilian-Australian researchers, led by the Royal Melbourne Institute of Technology, has developed an image processing algorithm that can detect fluids in the retina with an accuracy of 98%.

The algorithm provides a automatic analysis of fundus images to predict which people are at risk for diabetic retinopathy.

The researchers hope their method could one day be used for widespread screening among at-risk populations and are already in talks with fundus camera manufacturers to explore possible collaborations.

One of the diseases that the researchers highlight as a field of application for their technology is diabetes, since undiagnosed diabetes is a “massive” health problem throughout the world. In developing countries, the ratio is one diagnosed for every four undiagnosed, and this algorithm could allow massive test campaigns to be carried out that would help reduce that percentage and avoid the visual problems associated with this disease.

I hope you liked it and if you want to know more about this topic, below you will find the sources from which I have written this article:


Article and photo: Exudate detection in fundus images using deeply-learnable features

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