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To validate this approach, the model was applied to a new dataset and results are reported.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50\u201399% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS \u2013 0 -100 points) - previously developed and published - were measured.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original\/segmented images. There was a statistically significant albeit minor difference [0,19\u00a0mm (0,09\u20130,28)] regarding proximal border diameter. Overlap accuracy ((TP\u2009+\u2009TN)\/(TP\u2009+\u2009TN\u2009+\u2009FP\u2009+\u2009FN)), sensitivity (TP \/ (TP\u2009+\u2009FN)) and Dice Score (2TP \/ (2TP\u2009+\u2009FN\u2009+\u2009FP)) between original\/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87\u201396), similar to the previously obtained value in the training dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10554-023-02839-5","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T10:03:58Z","timestamp":1680861838000},"page":"1385-1396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model"],"prefix":"10.1007","volume":"39","author":[{"given":"Miguel","family":"Nobre Menezes","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o Louren\u00e7o","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Beatriz","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Cl\u00e1udio","family":"Guerreiro","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o Pedro","family":"Guedes","sequence":"additional","affiliation":[]},{"given":"Manuel Oliveira","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Arlindo L.","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Fausto J.","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,7]]},"reference":[{"key":"2839_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S12880-020-00509-9","volume":"201 20","author":"L Wang","year":"2020","unstructured":"Wang L, Liang D, Yin X et al (2020) Coronary artery segmentation in angiographic videos utilizing spatial-temporal information. 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