Data-Driven Modeling and Classification of Brain Blood-Flow Pathologies

Data-Driven Modeling and Classification of Brain Blood-Flow Pathologies Full article

Journal

AI
ISSN: 2673-2688

Output data

Year: 2026, Volume: 7, Number: 3, Article number : 105, Pages count : 17 DOI: 10.3390/ai7030105

Tags

data-driven modeling; diagnostics; hemodynamics; cerebral aneurysm; cerebral arteriovenous malformation

Authors

Topal Irem ^1,2 , Cherevko Alexander ³ , Bugai Yuriy ³ , Shishlenin Maxim ⁴ , Barbier Jean ⁵ , Eroglu Deniz ^6,2 , Roldán Édgar ⁵ , Belousov Roman ⁷

Affiliations

1	Department of Biomedical Sciences, University of Padova, 35131 Padova, Italy
2	Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul 34083, Türkiye
3	Lavrentyev Institute of Hydrodynamics, 630090 Novosibirsk, Russia
4	Sobolev Institute of Mathematics, 630090 Novosibirsk, Russia
5	ICTP—The Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy
6	Department of Mathematics, Imperial College London, London SW7 2AZ, UK
7	Cell Biology and Biophysics Unit, EMBL—European Molecular Biology Laboratory, 69117 Heidelberg, Germany

Funding (2)

1	Sobolev Institute of Mathematics	FWNF-2024-0001
2	Lavrentyev Institute of Hydrodynamics	FWGG-2021-0009-2.3.1.2.10

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine-learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving a balanced accuracy of 74%. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.

Topal I. , Cherevko A. , Bugai Y. , Shishlenin M. , Barbier J. , Eroglu D. , Roldán É. , Belousov R.
Data-Driven Modeling and Classification of Brain Blood-Flow Pathologies
AI. 2026. V.7. N3. 105 :1-17. DOI: 10.3390/ai7030105 OpenAlex

Submitted:	Dec 10, 2025
Accepted:	Mar 3, 2026
Published online:	Mar 11, 2026

≡ OpenAlex:

W7135049617