Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants
<b>Background</b>: 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. <b>Methods</b>: We...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/13/7/2089 |
| _version_ | 1797212343728340992 |
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| author | Seoyeon Park Junhyung Moon Hoseon Eun Jin-Hyuk Hong Kyoungwoo Lee |
| author_facet | Seoyeon Park Junhyung Moon Hoseon Eun Jin-Hyuk Hong Kyoungwoo Lee |
| author_sort | Seoyeon Park |
| collection | DOAJ |
| description | <b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| first_indexed | 2024-04-24T10:40:53Z |
| format | Article |
| id | doaj.art-1c4a2062552a466d9df2612be971f623 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-04-24T10:40:53Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-1c4a2062552a466d9df2612be971f6232024-04-12T13:21:30ZengMDPI AGJournal of Clinical Medicine2077-03832024-04-01137208910.3390/jcm13072089Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature InfantsSeoyeon Park0Junhyung Moon1Hoseon Eun2Jin-Hyuk Hong3Kyoungwoo Lee4Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of KoreaDepartment of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of KoreaDepartment of Pediatrics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seoul 03722, Republic of KoreaSchool of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Gwangju 61005, Republic of KoreaDepartment of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea<b>Background</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2077-0383/13/7/2089patent ductus arteriosuspremature infantdiagnostic support systemelectronic health recordmachine learning |
| spellingShingle | Seoyeon Park Junhyung Moon Hoseon Eun Jin-Hyuk Hong Kyoungwoo Lee Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants Journal of Clinical Medicine patent ductus arteriosus premature infant diagnostic support system electronic health record machine learning |
| title | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
| title_full | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
| title_fullStr | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
| title_full_unstemmed | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
| title_short | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
| title_sort | artificial intelligence based diagnostic support system for patent ductus arteriosus in premature infants |
| topic | patent ductus arteriosus premature infant diagnostic support system electronic health record machine learning |
| url | https://www.mdpi.com/2077-0383/13/7/2089 |
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