Machine learning applications in stroke medicine:advancements,challenges,and future prospectives
Mario Daidone1
Sergio Ferrantelli2
Antonino Tuttolomondo2
1.Internal Medicine and Stroke Care Ward,Department of Health Promotion,Mother and Child Care,Internal Medicine and Medical Specialties,University of Palermo,Palermo,Italy;Molecular and Clinical Medicine PhD Program,University of Palermo,Palermo,Italy2.Internal Medicine and Stroke Care Ward,Department of Health Promotion,Mother and Child Care,Internal Medicine and Medical Specialties,University of Palermo,Palermo,Italy
摘要：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.
机标关键词：applicationsmedicinelearningfuturestrokeadvancementschallengesmachine
论文发表日期：2024-04-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：5( 769-773 )
英文信息