Researchers have developed an artificial intelligence model capable of tracking a person’s sleep stages using only three ...

A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

An offline, point-of-care algorithm on a smartphone fundus camera generated disease-specific outputs without cloud connectivity, addressing a major deployment barrier in low-resource screening ...

One seismometer is often not enough to reliably detect earthquakes or human activity such as underground nuclear tests.

Imagine you're running and you sprain your ankle. The pain makes you gingerly limp the rest of the way home. This is a great ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

A deep learning-based real-time driver drowsiness detection and alert system using CNN-LSTM architecture. The model analyzes eye movements, mouth openness (yawning), and head pose to accurately ...

Claude Code generates computer code when people type prompts, so those with no coding experience can create their own programs and apps. By Natallie Rocha Reporting from San Francisco Claude Code, an ...

This study intends to bring onboard and execute a real-time drowsiness alert system using machine learning that will monitor the drivers' eye movement behaviours, thus, reducing the risk of road ...