Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Non-coding RNAs and other determinants of neuroinflammation and endothelial dysfunction:regulation of gene expression in the acute phase of ischemic stroke and possible therapeutic applications

Ischemic stroke occurs under a variety of clinical conditions and has different pathogeneses,resulting in necrosis of brain parenchyma.Stroke pathogenesis is characterized by neuroinflammation and endothelial dysfunction.Some of the main processes triggered in the early stages of ischemic damage are the rapid activation of resident inflammatory cells(microglia,astrocytes and endothelial cells),inflammatory cytokines,and translocation of intercellular nuclear factors.Inflammation in stroke includes all the processes mentioned above,and it consists of either protective or detrimental effects concerning the“polarization”of these processes.This polarization comes out from the interaction of all the molecular pathways that regulate genome expression:the epigenetic factors.In recent years,new regulation mechanisms have been cleared,and these include non-coding RNAs,adenosine receptors,and the activity of mesenchymal stem/stromal cells and microglia.We reviewed how long non-coding RNA and microRNA have emerged as an essential mediator of some neurological diseases.We also clarified that their roles in cerebral ischemic injury may provide novel targets for the treatment of ischemic stroke.To date,we do not have adequate tools to control pathophysiological processes associated with stroke.Our goal is to review the role of non-coding RNAs and innate immune cells(such as microglia and mesenchymal stem/stromal cells)and the possible therapeutic effects of their modulation in patients with acute ischemic stroke.A better understanding of the mechanisms that influence the“polarization”of the inflammatory response after the acute event seems to be the way to change the natural history of the disease.