Atrophy Pattern

Atrophy pattern analysis focuses on identifying and quantifying the characteristic tissue loss associated with neurodegenerative diseases like Alzheimer's disease and other conditions such as sarcopenia and age-related macular degeneration. Current research employs deep learning models, including convolutional neural networks (CNNs), autoencoders, and transformers, often coupled with techniques like deformable registration and shape analysis, to analyze medical images (MRI, OCT) and identify atrophy patterns with high accuracy. These advancements enable more precise diagnosis, disease monitoring, and potentially personalized treatment strategies by providing detailed spatial maps of atrophy and quantifying its progression over time. The improved accuracy and interpretability of these methods are crucial for clinical applications and accelerating research into neurodegenerative diseases.