USC researchers developed an AI model that uses MRI to produce detailed maps of local brain aging, revealing patterns linked to cognitive decline and Alzheimer's disease.
Key Details
- 1AI deep learning model trained on MRI scans from 14,748 cognitively healthy adults aged 19 to 100.
- 2Model analyzes local brain age at the voxel level for anatomical detail in aging patterns.
- 3Tested on data from over 1,900 participants, including those with mild cognitive impairment and Alzheimer's disease.
- 4Detected accelerated aging in brain regions tied to early neurodegeneration (e.g., hippocampus, amygdala).
- 5Stronger local brain age correlated with poorer cognitive assessment performance, especially in Alzheimer's patients.
- 6Model remains a research tool and needs validation in diverse clinical datasets before clinical adoption.
Why It Matters
This approach offers a more nuanced tool for early detection and monitoring of neurodegeneration, potentially paving the way for targeted interventions and personalized brain health management. Moving beyond a single 'brain age' metric could improve risk stratification and disease monitoring in radiology.
Source
EurekAlert
Related News
•EurekAlert
NYU AI Model Improves 5-Year Breast Cancer Risk Prediction via 3D Mammograms
NYU researchers developed an AI model using longitudinal 3D mammograms that outperforms single-scan and 2D-based tools in predicting 5-year breast cancer risk.
•EurekAlert
AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
•EurekAlert
Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.