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

## TL;DR

This mini-review explores how artificial intelligence can improve the detection of microembolic signals in ischemic stroke patients using transcranial Doppler.

## Contribution

The paper highlights recent AI applications for overcoming limitations in conventional TCD and improving stroke risk assessment.

## Key findings

- MES are linked to stroke recurrence and high-risk conditions like atherosclerosis and atrial fibrillation.
- AI enhances automated detection, artifact rejection, and signal interpretation in TCD.
- AI-based TCD systems may enable personalized stroke prevention strategies.

## Abstract

Microembolic signals (MES) detected by transcranial Doppler (TCD) provide real-time information on ongoing embolic activity in patients with ischemic stroke and transient ischemic attack. MES have been associated with stroke recurrence and high-risk conditions including large-artery atherosclerosis, atrial fibrillation, moyamoya disease, cancer-related stroke, and complex aortic arch plaques. Despite its clinical value, conventional TCD is limited by operator dependency, suboptimal acoustic windows, and limited ability to discriminate embolus characteristics. Recent advances in artificial intelligence (AI), including machine learning algorithms and robotic-assisted TCD systems, offer automated and reproducible MES detection, improved artifact rejection, and advanced signal interpretation. This mini-review summarizes the clinical relevance of MES, the main limitations of conventional TCD, and current and emerging applications of AI to MES detection, highlighting future perspectives for stroke risk stratification and personalized secondary prevention.