Hemodynamic Investigations in Intracranial Aneurysms: A Commentary: a commentary

Intracranial aneurysms (IAs) are abnormal bulges in a blood vessel in the brain that have a potential to rupture and even causing a stroke, which can lead to lasting brain damage, long-term disability, or even loss of life. It has been widely acknowledged that hemodynamic factors, e.g., instantaneous wall shear stress, time-averaged wall shear stress, wall shear stress gradient, gradient oscillatory number, oscillatory shear index, pulsatile blood flow waveform (flow rate magnitude and shape, physical flow period), relative residence time/turnover time, blood pressure, and vortex (i.e., size, location, and numbers), have a close relationship with the pathobiology (i.e., initiation, growth, and rupture) of IAs [1–9], apart from the ambiguous family history and genetic factors, and other non-modifiable factors (e.g., gender, age, at-risk disorders) [10]. Understanding the pathophysiology of IAs is critical for care decisions, precise hemodynamic information will more accurately identify new therapeutic targets and eliminate life-threatening conditions in the clinical practice. In fact, approximate 500 publications contributed to the IA research community annually, with an emphasis on the qualifications and/or quantifications of influences from hemodynamic impacts on the pathobiology of IAs, varying with case studies and/or statistical investigations, and have not been widely accepted as a clinical diagnostic pathway [11]. These studies summarized the continuous efforts in evaluating the hemodynamic factors on the IA pathophysiology, as well as the pre-/post-therapeutic treatments, including but not limited to the research on IA locations and morphologies, intervention work, physiological conditions and blood flow characteristics (i.e., blood pressure, arterial wall shear stress) via. the specific strategies in experiments (in-situ, in-vivo, in vitro), numerical modeling [1, 12–18], and physics informed machine leaning and deep learning (DL) driven by big data [19–22].