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We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2\u20134\u2009h per scan. Manually corrected segmentations had more branches (<jats:italic>p<\/jats:italic> &lt;\u20090.001), longer airways (<jats:italic>p<\/jats:italic> &lt;\u20090.001) and smaller luminal diameters (<jats:italic>p<\/jats:italic> = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.<\/jats:p>","DOI":"10.1186\/s41747-021-00247-9","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T02:02:37Z","timestamp":1638151357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Creating a training set for artificial intelligence from initial segmentations of airways"],"prefix":"10.1186","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7747-156X","authenticated-orcid":false,"given":"Ivan","family":"Dudurych","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Garcia-Uceda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaigham","family":"Saghir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harm A. 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The ImaLife study was registered with the Dutch Central Committee on Research Involving Human Subjects (, Identifier: NL58592.042.16)","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors of this manuscript declare no competing relationships with any companies, whose products or services may be related to the subject matter of the article. Harm Tiddens received consultancy fees from Thirona. Rozemarijn Vliegenthart is supported by an institutional research grant from Siemens Healthineers.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"54"}}