AI can predict biological age based on smartphone, wearables data

Artificial intelligence (AI) can produce digital biomarkers of ageing and frailty by gathering physical activity data from smartphones and other wearables, scientists have found.

The finding, published in the journal Scientific Reports, untaps the emerging potential of combining wearable sensors and AI technologies for continuous health risk monitoring with real-time feedback to life and health insurance, healthcare and wellness providers.

"Artificial Intelligence is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition, and other fields," said Peter Fedichev from the Moscow Institute of Physics and Technology (MIPT) in Russia.

"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 Fedichev.

The researches 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 ageing 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-ageing therapies," he said.