A new machine-learning algorithm is able to accurately detect cognitive impairment by analyzing voice recordings.
A neuroimaging-based machine learning algorithm can detect Alzheimer's in the brain with 98% accuracy. The system is also 79% accurate at determining which stage of Alzheimer's disease a patient has.
Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages.
Artificial intelligence can understand complex words and concepts by representing the meaning of words in a similar way that correlates with human judgments.
Artificial intelligence helps shed new light on why many with autism have a difficult time when it comes to processing emotions via facial expressions.
Using AI to analyze language associated with depression on social media during the first wave of the COVID-19 pandemic, researchers found people were more resilient than previously thought.
Before the 48-week mark of life, it is easier for an AI algorithm to determine the exact age of a baby, but not its gender based on temperament data. After 48 weeks, gender classification improved for all algorithms, suggesting gender differences in infancy become more accentuated at this point in life.
Using artificial intelligence technology, researchers have identified both risk and protective factors for depression in middle-aged to older adults. Social isolation, the study found, was the biggest risk factor for depression, followed by mobility difficulties and health issues.
Experimental observations conclude learning is mainly performed by neural dendrite trees as opposed to modifying solely through the strength of the synapses, as previously believed.
A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
A new deep learning algorithm utilizes neuroimaging data to differentiate between Parkinson's disease and other parkinsonian syndromes such as PSP and multiple systems atrophy.