Artificial intelligence predicts the sex of the brain!
Scientists at the Stanford University School of Medicine have developed an artificial intelligence model that can predict a person's sex with over 90% accuracy by looking at brain scans.
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A new study conducted by scientists at the Stanford University School of Medicine shows an artificial intelligence model that can determine whether a person is female or male with over 90% accuracy by evaluating scans of brain activity.
Published in the Proceedings of the National Academy of Sciences, this research, as reported by Popular Science Turkey, resolves a long-standing debate by revealing the existence of sex differences in the brain. Understanding these differences is seen as an important step in addressing neuropsychiatric conditions that may affect women and men differently.
Vinod Menon, director of the Stanford Cognitive and Systems Neuroscience Laboratory and professor of psychiatry and behavioral sciences, emphasizes the importance of the research as follows:
"One of the most important motivations for this study is that sex plays a very important role in human brain development, aging, and the manifestation of psychiatric and neurological disorders."
The researchers state that the artificial intelligence model is highly successful in distinguishing between female and male brains by using specific brain regions referred to as "hot spots." These regions include the default mode network and the striatum.
The researchers state that this new model shows that brain scans are quite effective in detecting sex differences and that these differences may not have been identified previously. It is noted that the model offers a comprehensive analysis by evaluating brain scans taken from different geographical regions.
Menon states that artificial intelligence models can help us understand the important factors underlying neuropsychiatric diseases by identifying sex differences in the brain. He also says that this model could be used to develop sex-specific models of cognitive abilities and could be used by researchers to understand how connectivity patterns in the brain can be linked to behaviors.
This research provides an important example of how artificial intelligence technology can be used in brain research and may contribute to a better understanding of neurological and psychiatric disorders in the future.