AI in Pediatric CHD

Summary

This project explores the transformative role of artificial intelligence, Federated Learning, and Explainable AI in diagnosing and managing congenital heart diseases (CHD) in pediatric patients. It focuses on conditions such as Hypoplastic Left Heart Syndrome (HLHS), Pulmonary Hypertension (PAH), Aortic Stenosis, Patent Ductus Arteriosus (PDA), and Ventricular Septal Defect (VSD).
Key applications in clinical use cases include:
Echocardiography Data Anonymization: Ensuring patient privacy by anonymizing echocardiographic data, which facilitates secure and compliant use of sensitive information.
Disease Classification: Utilizing AI to quickly and accurately classify various types of CHDs, thus improving diagnostic efficiency and aiding in timely treatment.
Segmentation of Cardiac Structures: Employing AI for precise segmentation of cardiac structures in echocardiograms, which enhances visualization and supports better treatment planning.
Quantitative Assessment of Cardiac Function: Applying AI to perform detailed quantitative analyses of cardiac function, enabling more informed and effective clinical decisions.
Large Language Models: Creating language models to interpret medical guidelines and patient health records, improving the accessibility and understanding of complex medical information.
By leveraging these advanced AI technologies, especially in echocardiography, healthcare professionals can achieve more accurate diagnoses, develop personalized treatment plans, and enhance outcomes for children with CHD.

Publications:

• Theodor Uden, Mohamed Yaseen J, Thomas Jack, Murat Avsar, Harald Bertram, Christoph Happel, Dagmar Hohmann, Alexander Horke, Claudia Junge, Sarah Long, Natalia Rudobashta, Kathrin Seidemann, Steffen Oeltze-Jafra, and Philipp Beerbaum, “A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions” Accepted for presentation at the European Society of Cardiology Congress, to be held in London, UK, August 2024.
• Mohamed Yaseen Jabarulla, Steffen Oeltze-Jafra, Philipp Beerbaum and Theodor Uden "MedDoc-Bot: A Chat Tool for Comparative Analysis of Large Language Models in the Context of the Pediatric Hypertension Guideline" Presented at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, to be held in Orlando, Florida, USA, July 15-19, 2024. DOI: arxiv.org/abs/2405.03359.
• MY Jabarulla, T Uden, P Beerbaum, S Oeltze-Jafra "Artificial intelligence in pediatric echocardiography-Automated view classification and image anonymization in rare cardiac malformations on the example of borderline HLHS" European Heart Journal, 44, Issue Supplement_2, 2023 (Impact Factor: 37.9) DOI: doi.org/10.1093/eurheartj/ehad655.061