WASHINGTON: Scientists who used artificial intelligence to determine biological age differences driven by smoking have found that smokers age much faster than their peers.
Smoking has long been proven to negatively affect people’s overall health in multiple ways.
“Smoking is a real problem that destroys people’s health, causes premature deaths, and is often the cause of many serious diseases,” said Polina Mamoshina, a senior research scientist at Insilico Medicine in the US.
“We applied artificial intelligence to prove that smoking significantly increases your biological age,” said Mamoshina.
The study, published in the journal Scientific Reports, set out to determine biological age differences between smokers and non-smokers.
The team evaluated the impact of smoking using blood biochemistry and recent advances in artificial intelligence.
By employing age-prediction models developed by supervised deep learning techniques, the study analysed a number of biochemical markers, including measures based on glycated hemoglobin, urea, fasting glucose and ferritin.
According to study’s results, smokers demonstrated a higher ageing ratio, and both male and female smokers were predicted to be twice as old as their chronological age as compared to nonsmokers.
The results were carried out based on the blood profiles of 149,000 adults.
Other findings suggested that deep learning analysis of routine blood tests could replace the current unreliable method of self-reporting of smoking status and evaluate the influence that other lifestyle and environmental factors have on ageing. (AGENCIES)internalising disorders with 81 per cent accuracy — better than the standard parent questionnaire.
“The way that kids with internalising disorders moved was different than those without,” said McGinnis.
The algorithm determined that movement during the first phase of the task, before the snake was revealed, was the most indicative of potential psychopathology.
Children with internalising disorders tended to turn away from the potential threat more than the control group.
It also picked up on subtle variations in the way the children turned that helped distinguish between the two groups.
This lines up well with what was expected from psychological theory, researchers said.
Children with internalising disorders would be expected to show more anticipatory anxiety, and the turning-away behaviour is the kind of thing that human observers would code as a negative reaction when scoring the video.
“Something that we usually do with weeks of training and months of coding can be done in a few minutes of processing with these instruments,” said Ellen McGinnis, a clinical psychologist at the University of Vermont.
The algorithm needs just 20 seconds of data from the anticipation phase to make its decision, she said.
The next step will be to refine the algorithm and develop additional tests to analyse voice data and other information that will allow the technology to distinguish between anxiety and depression.
The ultimate goal is to develop a battery of assessments that could be used in schools or doctors’ offices to screen children as part of their routine developmental assessments. (AGENCIES)
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