Year: 2025
Type: conference-paper
Abstract: Physics-Informed Neural Networks (PINNs) constitute a new type of neural architectures that incorporate governing physical laws and principles directly into the training process. This hybrid data-physics framework has attracted significant attention in domains where physical principles guide the interpretation and modeling of data, notably in neuroscience and brain imaging analysis (BIA). The cost of misinterpretation and misdiagnosis in this field is high, especially in the case of neurodegenerative disorders such as Alzheimer's disease (AD). PINNs offer a promising solution to persistent challenges, including the scarcity of labeled data, high noise levels, and the complexity of inverse problems. This paper reviews 9 recent studies, published between 2020 and 2025, that apply PINN-based models to brain imaging in AD. We focus on their integration into core tasks, such as image reconstruction, classification, predictive modeling, and biomarker analysis. We dive in-depth into the role of PINNs in diagnosing and monitoring AD, highlighting how they can support the detection of structural, functional, and metabolic brain alterations, contributing to earlier and more accurate diagnoses.
Authors: Hala Ali, Pascal Bourdon, Carole Guillevin, Christine Fernandez-Maloigne, Mohamad Khalil, David Helbert
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415934425