Combining machine learning technology with neuroimaging data, clinicians will be better able to fully analyze a patient's glioblastoma brain tumor and predict cancer progression.
A newly developed, open-source app that utilizes AI technology allows researchers to precisely map the structure of the hippocampus.
Machine learning algorithms can effectively recognize patterns in a patient's neuroimaging data that are specific to rare forms of dementia, allowing for early diagnosis and monitoring of disease progression.
Deep learning technology can accurately reflect a person's risk of cognitive decline and Alzheimer's disease based on brain age.
New AI technology can instantly determine whether a person is above the legal alcohol limit by analyzing a 12-second clip of their voice.
Using machine learning technology, researchers provide new insight into the neural mechanisms that govern anger and aggression.
OpenAI's ChatGPT program can identify clues from spontaneous speech that are 80% accurate in predicting the early stages of dementia.
Using advances in machine learning, researchers have created a new model that predicts the ease with which individuals produce and comprehend complex sentences.
New AI technology is able to detect changes to the retina and predict the risk of retinal vein occlusion. Researchers say the technology could also be used to detect heart attack and stroke risks.
"Off-line" periods during AI training mitigated "catastrophic forgetting" in artificial neural networks, mimicking the learning benefits sleep provides in the human brain.
A novel deep learning method that uses graph convolutional neural networks (gCNNs) can predict cognitive function based on the brain's size and structure. The algorithm may provide insights into the relationship between brain morphology and different cognitive functions, as well as declines in cognitive function.
New machine learning models assess the connection between hundreds of clinical variables, including doctor visits and health records for seemingly unconnected conditions, to predict the likelihood of ASD in young children.
