Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Neurology

Background:

• Brain stroke (cerebrovascular accident) is a leading cause of death and disability worldwide.
• Accurate prediction and diagnosis of stroke are crucial for effective management and patient outcomes.
• Existing analytical methods struggle with the complexity of stroke risk factors.

Purpose of the Study:

• To comprehensively review machine learning (ML) and deep learning (DL) applications in brain stroke diagnosis.
• To identify current challenges and future research directions in AI-driven stroke analysis.
• To provide a curated list of relevant datasets for brain stroke research.

Main Methods:

• Systematic review of 25 review papers published between 2020-2024, adhering to PRISMA guidelines.
• Focus on ML/DL applications in stroke classification, segmentation, and object detection.
• Evaluation of advanced sensor systems for predictive health monitoring.

Main Results:

• ML and DL techniques offer advanced data processing for identifying stroke predictors.
• These AI methods enhance diagnostic accuracy and enable personalized care recommendations.
• The review synthesizes findings on performance evaluation and validation of AI models.

Conclusions:

• AI, particularly ML and DL, holds significant potential to transform brain stroke diagnosis and patient care.
• Further research is needed to address current challenges and optimize AI applications in neurology.
• Advanced sensor systems integrated with AI can improve predictive health monitoring for stroke.