Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants

: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. : We introduce an artificial intelligence (AI)-based PDA dia...

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Veröffentlicht in:Journal of clinical medicine 2024-04, Vol.13 (7), p.2089
Hauptverfasser: Park, Seoyeon, Moon, Junhyung, Eun, Hoseon, Hong, Jin-Hyuk, Lee, Kyoungwoo
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Sprache:eng
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Zusammenfassung:: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. : We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. : The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. : Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm13072089