Researchers developed an AI model that accurately distinguishes between multiple dementia types using extensive, heterogeneous brain imaging data.
Key Details
- 1The model was trained and tested on 308,000 3D brain images from 17,000 patients collected over two decades.
- 2It detects vascular dementia, Alzheimer's, Lewy body dementia, Parkinson's, and mild cognitive impairment, with AUC >0.84 for these conditions.
- 3The dataset included multiple modalities (T1 MRI, T2 MRI, CT, PET), reflecting real-world clinical complexity and variation.
- 4The neural network is structured to handle varying numbers and types of images per patient (1–14), mitigating confounding variables like scanning site and age.
- 5Testing across multiple hospital sites demonstrated the model’s robustness to real-world heterogeneity.
- 6Future directions include larger datasets and development of explainable AI for neuroimaging disease detection.
Why It Matters
This study addresses a major barrier for clinical translation of imaging AI—performance on real-world, heterogeneous datasets—demonstrating a feasible path for deploying robust differential diagnostic tools across healthcare settings.
Source
EurekAlert
Related News
•EurekAlert
Chinese Researchers Unveil Photonic Chip for Ultra-Fast Image Processing
A new photonic chip achieves image processing at 25 million frames per second with high energy efficiency, promising major advances in real-time imaging and AI applications.
•EurekAlert
AI Model Predicts Growth Spurts from Pediatric Neck X-rays for Orthodontics
Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.
•EurekAlert
Dana-Farber Showcases AI and Clinical Trial Advances at ESMO 2025
Dana-Farber researchers present major cancer clinical trial results, including AI-driven data analysis, at ESMO Congress 2025.