Neural operator-based digital twins for modeling amyloid-$\beta$ and tau propagation and treatment optimization in Alzheimer's disease
arXiv:2606.25185v1 Announce Type: new Abstract: Accurately predicting the spatiotemporal evolution of amyloid-$\beta$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease. We consider the problem of constructing patient-specific digital twins that model the propagation of these biomarkers on the cortical surface using reaction--diffusion dynamics. A major challenge is that the underlying nonlinear aggregation mechanisms are unknown and must be inferred from sparse, noisy, and heterogeneous longitudinal PET imaging data. To addres
Advances in AI, particularly neural operators, are enabling more sophisticated and personalized modeling of complex biological processes from fragmented data.
This development represents a significant step towards precision medicine for neurodegenerative diseases, moving beyond generalized treatments to patient-specific interventions.
The ability to create patient-specific digital twins for diseases like Alzheimer's will fundamentally alter how diagnosis, prognosis, and treatment optimization are approached in neuroscience.
- · AI healthcare companies
- · Pharmaceutical companies developing Alzheimer's treatments
- · Patients with neurodegenerative diseases
- · Medical imaging technology providers
- · Traditional drug discovery models
- · Generic therapeutic approaches
- · Diagnostic methods relying solely on static biomarkers
Personalized digital twins will enable more effective and earlier intervention strategies for Alzheimer's disease.
The success in Alzheimer's could accelerate the application of neural operator-based digital twins to other complex chronic diseases.
This could lead to a paradigm shift in healthcare, emphasizing proactive, personalized, and predictive medical care driven by AI models.
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Read at arXiv cs.LG