Predicting Amyloid-beta Positivity in Cognitively Intact Adults Using Multi-contrast Three-dimensional MRI and Deep Learning (P10-13.002)

Pouria Saidi; Gina Dumkrieger; Justin Cramer; Yuxiang Zhou; Bryan Woodruff; Richard Caselli; Leslie Baxter; Oana Dumitrascu

doi:10.1212/wnl.0000000000215385

June 9, 2026Neurology

Predicting Amyloid-beta Positivity in Cognitively Intact Adults Using Multi-contrast Three-dimensional MRI and Deep Learning (P10-13.002)

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Key Points

The aim is to develop and test a model predicting CNS amyloid-β positivity using MRI data.
Developed a pretrained gated fusion model
Tested the model on two distinctive datasets
Utilized multi-contrast three-dimensional MRI scans
The model accurately predicts amyloid-beta positivity in cognitively normal individuals
Demonstrated robustness across two different datasets
Showed potential for enhancing early detection of cognitive decline

Implication

Develops a model predicting amyloid-beta positivity in cognitively intact adults, suggesting advanced diagnostic tools.

Abstract

To develop and test, in two distinctive datasets, a pretrained gated fusion model that predicts CNS amyloid-β (aβ) positivity from multi-contrast three-dimensional (3D) brain MRI in cognitively normal individuals.

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Cite This Study

Saidi et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3b4faef96ed7f056367 https://doi.org/10.1212/wnl.0000000000215385

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Predicting Amyloid-beta Positivity in Cognitively Intact Adults Using Multi-contrast Three-dimensional MRI and Deep Learning (P10-13.002)

Key Points

Implication

Abstract

Discussion

Cite This Study

Predicting Amyloid-beta Positivity in Cognitively Intact Adults Using Multi-contrast Three-dimensional MRI and Deep Learning (P10-13.002)

Key Points

Implication

Abstract

Discussion

Cite This Study

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