<description>&lt;p&gt;In this AI-generated episode of &lt;em&gt;Radiology AI Papers in a Capsule&lt;/em&gt;, we discuss a study that extends the NeuroHarmony AI model to address scanner variability in brain MRI for Alzheimer's disease assessment. Learn how incorporating cognitive status into harmonization may improve the reliability of quantitative imaging across diverse clinical settings.&lt;/p&gt; &lt;p&gt;&lt;a href="https://doi.org/10.1148/ryai.240030"&gt;A Machine Learning Model to Harmonize Volumetric BrainMRI Data for Quantitative Neuroradiologic Assessment ofAlzheimer Disease. Archetti and Venkatraghavan et al. Radiology: Artificial Intelligence 2025; 7(1):e240030.&lt;/a&gt;&lt;/p&gt;</description>

Radiology AI Podcast | RSNA

Radiological Society of North America (RSNA)

Radiology AI Papers in a Capsule Series-Episode 2

MAY 30, 202511 MIN
Radiology AI Podcast | RSNA

Radiology AI Papers in a Capsule Series-Episode 2

MAY 30, 202511 MIN

Description

In this AI-generated episode of Radiology AI Papers in a Capsule, we discuss a study that extends the NeuroHarmony AI model to address scanner variability in brain MRI for Alzheimer's disease assessment. Learn how incorporating cognitive status into harmonization may improve the reliability of quantitative imaging across diverse clinical settings. A Machine Learning Model to Harmonize Volumetric BrainMRI Data for Quantitative Neuroradiologic Assessment ofAlzheimer Disease. Archetti and Venkatraghavan et al. Radiology: Artificial Intelligence 2025; 7(1):e240030.