Eye Scan Predicts Mortality Risk

Using deep learning to predictretinal age” from images of the internal surface of the back of the eye, an international team of scientists has found that the difference between the biological age of an individual’s retina and that person’s real, chronological age, is linked to their risk of death. This ‘retinal age gap’ could be used as a screening tool, the investigators suggest.

Reporting on development of their deep learning model and research results in the British Journal of Ophthalmology, first author Zhuoting Zhu, PhD, at Guangdong Academy of Medical Sciences, together with colleagues at the Centre for Eye Research Australia, Sun Yat-Sen University, and colleagues in China, Australia, and Germany, concluded that in combination with previous research, their study results add weight to the hypothesis that “… the retina plays an important role in the aging process and is sensitive to the cumulative damages of aging which increase the mortality risk.”

Estimates suggest that the global population aged 60 years and over will reach 2.1 billion in 2050, the authors noted.

Aging populations place tremendous pressure on healthcare systems.

But while the risks of illness and death increase with age, these risks vary considerably between different people of the same age, implying that ‘biological aging’ is unique to the individual and may be a better indicator of current and future health. As the authors pointed out, “Chronological age is a major risk factor for frailty, age-related morbidity and mortality. However, there is great variability in health outcomes among individuals with the same chronological age, implying that the rate of aging at an individual level is heterogeneous. Biological age rather than chronological age can better represent health status and the aging process.

Several tissue, cell, chemical, and imaging-based indicators have been developed to pick up biological aging that is out of step with chronological aging. But these techniques are fraught with ethical/privacy issues as well as often being invasive, expensive, and time consuming, the researchers noted.

Source: https://www.genengnews.com/

AI Predicts Heart Attacks

In a study published Feb. 14 in Circulation, researchers in the U.K. and the U.S. report that an AI program can reliably predict heart attacks and strokes. Kristopher Knott, a research fellow at the British Heart Foundation, and his team conducted the largest study yet involving cardiovascular magnetic resonance imaging (CMR) and AI. CMR is a scan that measures blood flow to the heart by detecting how much of a special contrast agent heart muscle picks up; the stronger the blood flow, the less likely there will be blockages in the heart vessels. Reading the scans, however, is time consuming and laborious; and it’s also more qualitative than quantitative, says Knott, subject to the vagaries of the human eyes and brain. To try to develop a more qualitative tool, Knott and his colleagues trained an AI model to read scans and learn to detect signs of compromised blood flow.

When they tested the technology on the scans of more than 1,000 people who needed CMR because they either at risk of developing heart disease or had already been diagnosed, they found the AI model worked pretty well at selecting out which people were more likely to go on to have a heart attack or stroke, or die from one. The study compared the AI-based analyses to health outcomes from the patients, who were followed for about 20 months on average. The researchers discovered that for every 1 ml/g/min decrease in blood flow to the heart, the risk of dying from a heart event nearly doubled, and the risk of having a heart attack, stroke or other event more than doubled.

Rather than a qualitative view of blood flow to the heart muscle, we get a quantitative number,” he says. “And from that number, we’ve shown that we can predict which people are at higher risk of adverse events.”

The study confirmed that CMR is a strong marker for risk of heart problems, but did not prove that the scans could actually be used to guide doctors’ decisions about which people are at higher risk. For that, more studies need to be done that document whether treating poor blood flow—with available medication or procedures—in people with decreased flow as predicted by the AI model, can reduce or eliminate heart attacks and strokes.

Source: https://time.com/

Facial Recognition And AI Identify 90% Of Rare Genetic Disorders

A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses.

In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.

Rare genetic disorders are collectively common, affecting 8 percent of the population

In two first tests, DeepGestalt was used to look for specific disorders: Cornelia de Lange syndrome and Angelman syndrome. Both of these are complex conditions that affect intellectual development and mobility. They also have distinct facial traits, like arched eyebrows that meet in the middle for Cornelia de Lange syndrome, and unusually fair skin and hair for Angelman syndrome.

When tasked with distinguishing between pictures of patients with one syndrome or another, random syndrome, DeepGestalt was more than 90 percent accurate, beating expert clinicians, who were around 70 percent accurate on similar tests. When tested on 502 images showing individuals with 92 different syndromes, DeepGestalt identified the target condition in its guess of 10 possible diagnoses more than 90 percent of the time.

Source: https://www.theverge.com