AI in Pediatric CHD

AI in Pediatric CHD

Artificial Intelligence in Pediatric Echocardiography for Congenital Heart Diseases

Contact

Mohamed Yaseen Jabarulla

Project partners

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 ultimately enhance outcomes for children with CHD.

Publications:

Duration

03/2022 - Present

Staff

Dr. med. Theodor Uden, Research Associate and Co-Group Leader of the Project, Pediatric Cardiology and Pediatric Intensive Care Medicine, MHH.
Prof. Dr. med. Philipp Beerbaum, Director of the Clinic for Pediatric Cardiology, MHH.
Sarah Elizabeth Long, Scientific Associate, MHH.
Mohammed Saeed Ali Saif, Research Associate, MHH.
Khikmat Mirmukhamedov, MS Topic under DigiStrucMed Project 2023.
Helene Rosmus, PhD Topic under DigiStrucMed Project 2023.

Former Members
Natalia Rudobashta, Freiwilliges Jahr in der Wissenschaft (FWJ) (04/2023- 05/2024)
Rupp-Pardos, Valentin, Intern (12/2023 - 01/2024)