AI can better predict biological age via smartphone data

Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests.

According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields. 

"Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said co-author Peter Fedichev, Science Director at GERO.
"Inspired by these examples, we explored AI potential for 'Health Risks Assessment' based on human physical activity," Fedichev added.

For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES). 

They trained neural network to predict biological age and mortality risk of the participants from one week long stream of activity measurements. 

A state-of-the-art 'Convolution Neural Network' was used to unravel the most biologically relevant motion patterns and establish their relation to general health and recorded lifespan.

"We report that AI can be used to further refine the risks models," Fedichev said.
"Combination of aging theory with the most powerful modern machine learning tools will produce even better health risks models to mitigate longevity risks in insurance, help in pension planning, and contribute to upcoming clinical trials and future deployment of anti-aging therapies," Fedichev noted.