A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning...

Area of Science:

• Neurology
• Medical Imaging
• Artificial Intelligence

Background:

• Intracranial aneurysms pose a significant risk, with rupture leading to potentially fatal subarachnoid hemorrhage.
• Early detection of subarachnoid hemorrhage is crucial for timely intervention and improved patient survival rates.
• Despite advancements, challenges remain in understanding aneurysm pathophysiology and managing unruptured cases.

Approach:

• Develop and evaluate machine and deep learning models for aneurysm and hemorrhage detection.
• Analyze various hemorrhage types and diagnostic challenges.
• Integrate physiological/imaging markers and hemodynamic data for enhanced analysis.

Key Points:

• Aneurysm screening reveals significant detection rates, highlighting the need for advanced diagnostic tools.
• Subarachnoid hemorrhage, a common complication, presents a high risk of severe neurological deficits or death.
• Artificial intelligence offers promising potential for precise and reliable analysis of these complex conditions.

Conclusions:

• Machine and deep learning models show promise in improving the accuracy and efficiency of aneurysm and hemorrhage detection.
• Further research into pathophysiology and AI integration is essential for advancing clinical management.
• Enhanced diagnostic capabilities can lead to better patient outcomes and reduced mortality associated with these cerebrovascular events.