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New AI model enhances brain age estimation using MRI and blood flow data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel multimodal framework to enhance the accuracy of brain age estimation, a biomarker for neurobiological aging and disease risk. This new approach combines predictions from two distinct 3D convolutional neural networks: one analyzing structural MRI scans and the other processing synthesized cerebral blood volume (DeepCBV) maps. The combined model achieved a mean absolute error of 3.95 years in estimating brain age for healthy controls, outperforming models that used only MRI or DeepCBV data. AI

IMPACT This AI-driven approach could improve early detection and risk stratification for neurodegenerative diseases like Alzheimer's.

RANK_REASON The cluster contains an academic paper detailing a new methodology for brain age estimation using AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI model enhances brain age estimation using MRI and blood flow data

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The cluster contains an academic paper detailing a new methodology for brain age estimation using AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jordan Jomsky, Zongyu Li, Kay C. Igwe, Yiren Zhang, Max Lashley, Tal Nuriel, Andrew Laine, Scott A. Small, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative ·

    Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps

    arXiv:2412.01865v5 Announce Type: replace-cross Abstract: BrainAGE is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may preced…

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