A framework for AI-assisted detection of Patent Ductus Arteriosus from neonatal phonocardiogram

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dc.contributor.authorGómez-Quintana, Sergi
dc.contributor.authorSchwarz, Christoph E.
dc.contributor.authorShelevytsky, Ihor
dc.contributor.authorShelevytska, Victoriya
dc.contributor.authorSemenova, Oksana
dc.contributor.authorFactor, Andreea
dc.contributor.authorPopovici, Emanuel
dc.contributor.authorTemko, Andriy
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderDeutsche Forschungsgemeinschaften
dc.contributor.funderWellcome Trusten
dc.contributor.funderGrand Challenges Canadaen
dc.date.accessioned2022-08-03T14:39:40Z
dc.date.available2022-08-03T14:39:40Z
dc.date.issued2021-02-05
dc.date.updated2022-08-03T14:28:01Z
dc.description.abstractThe current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.en
dc.description.sponsorshipScience Foundation Ireland (Centre for Research Training in Artificial Intelligence under Grant No 18/CRT/6223; TIDA 17/TIDA/504); Wellcome Trust Seed Award 200704/Z/16/Z); Grand Challenges Canada (R-ST-POC-1707-07709); Deutsche Forschungsgemeinschaft (420536451)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid169en
dc.identifier.citationGómez-Quintana, S., Schwarz, C. E., Shelevytsky, I., Shelevytska, V., Semenova, O.,Factor, A., Popovici, E. and Temko, A. (2021) 'A framework for AI-assisted detection of Patent Ductus Arteriosus from neonatal phonocardiogram', Healthcare, 9(2), 169 (19pp). doi: 10.3390/healthcare9020169en
dc.identifier.doi10.3390/healthcare9020169en
dc.identifier.eissn2227-9032
dc.identifier.endpage19en
dc.identifier.issued2en
dc.identifier.journaltitleHealthcareen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13450
dc.identifier.volume9en
dc.language.isoenen
dc.publisherMDPIen
dc.rights© 2021, the Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectHeart sounden
dc.subjectNeonatesen
dc.subjectCongenital heart defectsen
dc.subjectMachine learningen
dc.subjectBoosted decision treesen
dc.subjectPatent ductus arteriosusen
dc.subjectPhonocardiogramen
dc.titleA framework for AI-assisted detection of Patent Ductus Arteriosus from neonatal phonocardiogramen
dc.typeArticle (peer-reviewed)en
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