{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:33:47Z","timestamp":1776821627417,"version":"3.51.2"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"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. Cardiovasc. Med."],"abstract":"<jats:p>Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator\u2019s experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.<\/jats:p>","DOI":"10.3389\/fcvm.2023.1056055","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T17:13:45Z","timestamp":1676394825000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Assisted probe guidance in cardiac ultrasound: A review"],"prefix":"10.3389","volume":"10","author":[{"given":"Sofia","family":"Ferraz","sequence":"first","affiliation":[]},{"given":"Miguel","family":"Coimbra","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Pedrosa","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","article-title":"Deep learning for cardiac image segmentation: a review","volume":"7","author":"Chen","year":"2020","journal-title":"Front Cardiovasc 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