Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention

Area of Science:

• Neurosurgery
• Cardiovascular Medicine
• Artificial Intelligence

Background:

• Platelets are crucial for hemostasis and thrombosis.
• Antiplatelet therapy is essential for preventing cardiovascular events.
• Neurosurgery presents unique challenges for antiplatelet therapy management.

Purpose of the Study:

• To review current guidelines for antiplatelet therapy in neurosurgery.
• To explore monitoring methods like PRU and TEG for antiplatelet efficacy.
• To investigate the potential of AI in optimizing antiplatelet therapy decisions and patient adherence in neurosurgery.

Main Methods:

• Review of clinical guidelines and supported antiplatelet regimens.
• Exploration of P2Y12 reaction units (PRU) and thromboelastography (TEG) monitoring.
• Analysis of AI prediction models for forecasting ischemic and bleeding events.
• Consideration of AI for improving patient adherence.

Main Results:

• Current guidelines for antiplatelet therapy in neurosurgery are limited.
• PRU and TEG monitoring have limitations in assessing antiplatelet response.
• AI models show promise in predicting adverse outcomes and aiding treatment decisions.
• AI can potentially enhance patient adherence to antiplatelet regimens.

Conclusions:

• Optimizing antiplatelet therapy in neurosurgery requires careful risk-benefit assessment.
• AI holds significant potential to personalize antiplatelet treatment strategies.
• Further research into AI applications can improve patient safety and outcomes in neurosurgical settings.