{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T02:40:21Z","timestamp":1789958421640,"version":"4.0.1"},"reference-count":8,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fneur.2026.1758938","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T07:19:01Z","timestamp":1769411941000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence for microembolic signal detection by transcranial Doppler in ischemic stroke: a mini-review"],"prefix":"10.3389","volume":"17","author":[{"given":"Davide","family":"Sassos","sequence":"first","affiliation":[{"name":"IRCCS Ospedale Policlinico San Martino","place":["Genoa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Del Sette","sequence":"additional","affiliation":[{"name":"IRCCS Ospedale Policlinico San Martino","place":["Genoa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1161\/STROKEAHA.122.040073","article-title":"Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030","volume":"54","author":"Pu","year":"2023","journal-title":"Stroke"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/S1474-4422(13)70310-7","article-title":"Embolic strokes of undetermined source: the case for a new clinical construct","volume":"13","author":"Hart","year":"2014","journal-title":"Lancet Neurol"},{"key":"ref3","first-page":"35","article-title":"Classification of subtype of acute ischemic stroke: definitions for use in a multicenter clinical trial. TOAST","volume-title":"Stroke","author":"Adams","year":"1993"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1177\/23969873221133912","article-title":"Microembolic signal monitoring in stroke subtypes: a systematic review and meta-analysis of 58 studies","volume":"6","author":"Sudheer","year":"2021","journal-title":"Eur Stroke J"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1177\/23969873231226417","article-title":"Detection of microemboli in patients with acute ischaemic stroke and atrial fibrillation suggests poor functional outcome","volume":"9","author":"Castro","year":"2024","journal-title":"Eur Stroke J"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.18700\/jnc.200002","article-title":"Robotically assisted transcranial Doppler with artificial intelligence for assessment of cerebral vasospasm after subarachnoid hemorrhage","volume":"13","author":"Esmaeeli","year":"2020","journal-title":"J Neurocrit Care"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"681","DOI":"10.3390\/bioengineering12070681","article-title":"Italian Society of Neurosonology and Cerebral Hemodynamics (SINSEC) and SINSEC Study Group for Artificial Intelligence in Neurosonology Artificial intelligence in the diagnostic use of transcranial Doppler and sonography: a scoping review of current applications and future directions","volume":"12","author":"Miceli","year":"2025","journal-title":"Bioengineering (Basel)"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1038\/s41582-022-00614-z","article-title":"Review and update of the concept of embolic stroke of undetermined source","volume":"18","author":"Diener","year":"2022","journal-title":"Nat Rev Neurol"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1758938\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T07:19:05Z","timestamp":1769411945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1758938\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,26]]},"references-count":8,"alternative-id":["10.3389\/fneur.2026.1758938"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2026.1758938","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,26]]},"article-number":"1758938"}}