Machine learning algorithm produced fewer decision-making errors than professionals when it came to clinical diagnosis of patients.
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··3 min readNovel AI technology allows researchers to understand which brain regions directly interact with each other, which helps guide the placement of electrodes for DBS to treat neurological diseases.
A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans.
A new artificial intelligence algorithm analyzes personal risk factors to accurately predict stroke recurrence in patients.
··3 min read
A new AI algorithm can independently discover and categorize an animal's behavior by analyzing patterns of body movements.
Researchers discuss different current neural network models and consider the steps that need to be taken to make them more realistic, and thus more useful, as possible.
Combining artificial intelligence technology with raw data from brain activity, researchers accelerate the understanding of how neural activity impacts specific behaviors.
A new AI model can accurately classify a brain tumor of one of six common cancer types from a single MRI brain scan image.
Artificial neural networks modeled on human brain connectivity can effectively perform complex cognitive tasks.
Artificial neural networks help researchers uncover new clues as to why people on the autism spectrum have trouble interpreting facial expressions.
Combining artificial intelligence, mathematical modeling, and brain imaging data, researchers shed light on the neural processes that occur when people use mental abstraction.
Blood tests revealed specific epigenetic biomarkers for schizophrenia. Researchers applied machine learning to analyze the CoRSIVs region of the human genome to identify the schizophrenia biomarkers. Testing the model with an independent data set revealed the AI technology can detect schizophrenia with 80% accuracy.
