An artificial intelligence tool can detect autism spectrum disorder with 100-percent accuracy, just by scanning images of children's eyes, according to a new study. If confirmed, this would be a major breakthrough for detecting the condition. But multiple autism experts told DailyMail.com Autism affects an estimated 1 in 36 children in the US, but many children remain undiagnosed until later in childhood, depriving them of potential therapies. If a technological solution could help cut down on long waits for autism specialists or other obstacles to diagnosis, it could benefit millions of families.

You are free to share this article under the Attribution 4.0 International license. A form of artificial intelligence designed to interpret a combination of retinal images successfully identified a group of patients known to have Alzheimer's disease, researchers report. The findings suggest the approach could one day be used as a predictive tool, according to the new study. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals.

Last week, at a conference in London, Dr Pearse Keane beamed an image onto the wall of an orange globe with a dark centre, encircled by red storms and a bright moon. It looked like a dying planet in a distant galaxy. In fact, it was a beautifully detailed scan of the back of a human eye, as awesome in its way as the night sky. These days, Dr Keane said, that single image betrays a lot of information. "We can now look at a retinal photograph and say: 'This is a woman.

Scientists have developed an artificial intelligence (AI) that can identify people by measuring their gait or walking pattern. The technology could be used at airport security instead of fingerprinting and eye-scanning. It can successfully verify an individual simply by analysing the footstep 3D and time-based data. The AI system, developed by researchers at University of Manchester in the UK and University of Madrid in Spain, correctly identified an individual almost 100 per cent of the time, with just a 0.7 error rate. Physical biometrics, such as fingerprints, facial recognition and retinal scans, are currently more commonly used for security purposes.

A new algorithm developed by Google and its sibling-company Verily Life Sciences can assess a person's risk of cardiovascular diseases. The AI scans images of a person's retina and accurately predicts the risk of major cardiac events such as heart attack or stroke. The robotic AI uses standard retinal scans to analyze the blood vessels in the retina. The study led by researchers from Google, Verily Life Sciences, and the Stanford School of Medicine used deep-learning algorithms to extract new knowledge from retinal images. The AI scanned the retinal images and identified cardiovascular risk factors based on age, gender, blood pressure, and smoking status, among others.

The retina is a thin layer of nerves lining the back of your eyes that sends signals to your brain, enabling you to see. We doctors have always referred to it as the window to the brain. Now, thanks to a remarkable new advance, there is reason to consider the retina as a window to the heart as well – and someday it might give your doctor information that could save your life. Google Artificial Intelligence and its subsidiary, Verily Life Sciences, have assembled a database of retinal scans from nearly 300,000 patients and used it to develop an algorithm to predict the risk of developing heart disease over five years. These results are dramatically positive and the beginning of a new and effective approach to prevention. The scan analyzes telltale signs of aging, smoking and high blood pressure found in the tiny vessels and structures of the retina.

AI researchers at Google have developed algorithms that can assess the risk of heart attacks by analyzing retinal scans. By looking for common patterns in images of retinal scans and matching them up with the data in the patients' medical records, one algorithm could determine if someone was a smoker or non-smoker to an accuracy of 71 per cent. Another algorithm focused on the blood vessels in the eye could tell if someone had severe high blood pressure or not, a sign associated with increased chances of stroke. Their models can also predict other factors such as age, gender, and the chance of a heart attack or stroke, the boffins claim in a paper published in Nature Biomedical Engineering journal on Monday. "Given the retinal image of one patient who (up to 5 years) later experienced a major [cardiovascular] event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the cardiovascular event 70% of the time," Lily Peng, a product manager at Google Brain, explained in a blog post this week.

Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these problems. What's more, the new system shows what parts of the eye image lead to successful predictions, giving researchers new leads into what causes cardiovascular disease. The results of the Google AI research have been published in an article entitled "Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning" in Nature Biomedical Engineering. "Using deep learning algorithms trained on data from 284,335 patients, we were able to predict CV risk factors from retinal images with surprisingly high accuracy for patients from two independent data sets of 12,026 and 999 patients," Lily Peng, MD, product manager and a lead on these efforts within Google AI, wrote in the Google AI official blog.