Academic Journal
Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants
| العنوان: | Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants |
|---|---|
| المؤلفون: | Seoyeon Park, Junhyung Moon, Hoseon Eun, Jin-Hyuk Hong, Kyoungwoo Lee |
| المصدر: | Journal of Clinical Medicine, Vol 13, Iss 7, p 2089 (2024) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | patent ductus arteriosus, premature infant, diagnostic support system, electronic health record, machine learning, Medicine |
| الوصف: | Background : 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. Methods : 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. Results : 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. Conclusions : 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2077-0383 |
| Relation: | https://www.mdpi.com/2077-0383/13/7/2089; https://doaj.org/toc/2077-0383; https://doaj.org/article/1c4a2062552a466d9df2612be971f623 |
| DOI: | 10.3390/jcm13072089 |
| الاتاحة: | https://doi.org/10.3390/jcm13072089 https://doaj.org/article/1c4a2062552a466d9df2612be971f623 |
| رقم الانضمام: | edsbas.8B5C394F |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20770383 |
|---|---|
| DOI: | 10.3390/jcm13072089 |