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Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve \u2013 achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve \u2013 which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.<\/jats:p>","DOI":"10.1186\/s13040-024-00381-1","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T07:02:58Z","timestamp":1724828578000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning for automatic calcium detection in echocardiography"],"prefix":"10.1186","volume":"17","author":[{"given":"Lu\u00eds B.","family":"Elvas","sequence":"first","affiliation":[]},{"given":"Sara","family":"Gomes","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o C.","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds Br\u00e1s","family":"Ros\u00e1rio","sequence":"additional","affiliation":[]},{"given":"Tom\u00e1s","family":"Brand\u00e3o","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"381_CR1","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1016\/j.csbj.2022.03.025","volume":"20","author":"J-S Hong","year":"2022","unstructured":"Hong J-S, et al. Automated coronary artery calcium scoring using nested U-Net and focal loss. Comput Struct Biotechnol J. 2022;20:1681\u201390. https:\/\/doi.org\/10.1016\/j.csbj.2022.03.025.","journal-title":"Comput Struct Biotechnol J"},{"issue":"25","key":"381_CR2","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","volume":"76","author":"GA Roth","year":"2020","unstructured":"Roth GA, et al. Global Burden of Cardiovascular diseases and Risk factors, 1990\u20132019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982\u20133021. https:\/\/doi.org\/10.1016\/j.jacc.2020.11.010.","journal-title":"J Am Coll Cardiol"},{"key":"381_CR3","doi-asserted-by":"publisher","unstructured":"Baumgartner H et al. (chair) Mar., \u2018Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European Association of Cardiovascular Imaging and the American Society of Echocardiography\u2019, Eur. Heart J. - Cardiovasc. 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They were informed about the purpose of the research, potential risks, and their rights. Participants were assured of confidentiality, and their data will be used only for research purposes.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent of publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27"}}