Machine learning for cerebral blood vessels' malformations
This work addresses the challenge of risk assessment and prognosis for cerebral aneurysms and arteriovenous malformations, offering a potentially interpretable tool for medical interventions, though it appears incremental as it builds on existing methods like SINDy and logistic regression.
The researchers tackled the problem of assessing life-threatening cerebral blood vessel pathologies by developing a linear oscillatory model that uses SINDy to reconstruct parameters from hemodynamic data, achieving 73% accuracy in automated classification with logistic regression.
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 an accuracy of 73 \%}. 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.