Artificial intelligence for microembolic signal detection by transcranial Doppler in ischemic stroke: a mini-review

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cardiovascular Medicine

Background:

• Microembolic signals (MES) detected via transcranial Doppler (TCD) indicate embolic activity in ischemic stroke and transient ischemic attack patients.
• MES are linked to stroke recurrence and conditions like large-artery atherosclerosis, atrial fibrillation, and cancer-related stroke.
• Conventional TCD methods face limitations including operator dependency and poor acoustic windows, hindering accurate embolus characterization.

Purpose of the Study:

• To review the clinical significance of MES in stroke.
• To outline the limitations of traditional TCD for MES detection.
• To explore the role of artificial intelligence (AI) in advancing MES detection and interpretation for stroke management.

Main Methods:

• Review of current literature on MES detection using transcranial Doppler (TCD).
• Analysis of limitations associated with conventional TCD techniques.
• Examination of emerging AI applications, including machine learning and robotic systems, for MES analysis.

Main Results:

• MES detection provides critical real-time data on embolic events relevant to stroke recurrence.
• AI-powered TCD systems demonstrate potential for automated, reproducible MES detection and artifact reduction.
• AI facilitates advanced signal interpretation, offering enhanced capabilities beyond conventional TCD.

Conclusions:

• AI integration in TCD holds significant promise for improving the accuracy and efficiency of MES detection.
• AI-driven MES analysis can enhance stroke risk stratification and personalize secondary prevention strategies.
• Future research should focus on validating AI tools for widespread clinical adoption in stroke care.