Medical Data ScienceOpen OpportunitiesPulmonary hypertension (PH) in newborns poses significant diagnostic challenges due to its association with various diseases and its impact on morbidity and mortality. Early and accurate detection is essential for effective management, yet current manual echocardiographic assessment is time-consuming and requires expertise. This project aims to develop an automated machine learning method using multimodal variational autoencoders (VAEs) and diffusion models to predict PH in newborns from ultrasound, ECG data, and clinical variables. Leveraging a cohort of 270 newborns from the University Children’s Hospital Regensburg, the project will enhance interpretability and feature representation by assessing the significance of each data type and utilizing synthetic data augmentation. The hybrid approach of combining VAEs with diffusion models is expected to improve prediction accuracy and generalization, advancing early detection and understanding of PH in newborns. - Biomedical Engineering, Computer Communications Networks, Electrical and Electronic Engineering, Image Processing, Signal Processing
- ETH Zurich (ETHZ), Master Thesis
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