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Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants
Seoyeon Park1, Junhyung Moon1, Hoseon Eun2
1Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea.
Insights
An AI system aids in diagnosing patent ductus arteriosus (PDA) in premature infants, improving early detection. This technology assists medical professionals, enhancing diagnostic accuracy and timeliness for vulnerable newborns.
Area of Science:
- Neonatal cardiology
- Medical artificial intelligence
- Congenital heart defects
Background:
- Patent ductus arteriosus (PDA) is a common congenital heart defect in premature infants, leading to severe health issues.
- Timely and accurate diagnosis of PDA is critical for improving outcomes in vulnerable premature infants.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI)-based diagnostic support system for PDA in premature infants.
- To assess the system's ability to improve the accuracy and timeliness of PDA diagnosis.
Main Methods:
- Utilized electronic health record (EHR) data from 409 premature infants over ten years.
- Developed an AI system integrating data viewing, analysis, and AI-based diagnosis support.
- Evaluated the system's performance through diagnostic tests with medical professionals.
Main Results:
- The AI-based system achieved up to 84% accuracy in early PDA detection, identifying it up to 3.3 days sooner.
- Medical professionals using the AI support system demonstrated improved diagnostic performance compared to those without it.
Conclusions:
- The AI-based PDA diagnostic support system provides a valuable tool for accurate and timely diagnosis in premature infants.
- Integrating AI with clinical expertise enhances neonatal diagnostic capabilities and patient care.
Abstract:
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.
