this post was submitted on 27 Feb 2024
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92
AI Determines Sex of Person From Brain Scans - Neuroscience News (neurosciencenews.com)
submitted 8 months ago by Squire1039@lemm.ee to c/technology@lemmy.world
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This article describes a new study using AI to identify sex differences in the brain with over 90% accuracy.

Key findings:

  • An AI model successfully distinguished between male and female brains based on scans, suggesting inherent sex-based brain variations.
  • The model focused on specific brain networks like the default mode, striatum, and limbic networks, potentially linked to cognitive functions and behaviors.
  • These findings could lead to personalized medicine approaches by considering sex differences in developing treatments for brain disorders.

Additional points:

  • The study may help settle a long-standing debate about the existence of reliable sex differences in the brain.
  • Previous research failed to find consistent brain indicators of sex.
  • Researchers emphasize that the study doesn't explain the cause of these differences.
  • The research team plans to make the AI model publicly available for further research on brain-behavior connections.

Overall, the study highlights the potential of AI in uncovering previously undetectable brain differences with potential implications for personalized medicine.

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[–] Chuymatt@kbin.social 3 points 8 months ago (2 children)

10% seems a bit more than was predicted, but would that account for those who don’t fit the peaks for the sexual dimorphism definitions, you think?

[–] RobotToaster@mander.xyz 1 points 8 months ago

IIRC Simon Baron Cohen's studies on his (IMHO poorly named) extreme male brain theory of autism found that only 70% of men had "male brains".

[–] knightly@pawb.social 1 points 8 months ago* (last edited 8 months ago)

I think so. With a more diverse dataset and fewer binary assumptions baked into the analysis I think we'd start seeing the bimodal contours of a spectrum between the masculine and feminine peaks. The graphics included in the study seem to hint at this, showing nodes of similarity with a tapering tail toward the middle of the distribution for all three sets of data they analyzed: