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Approved Research

Prediction of clinical outcomes in patients with aortic valve regurgitation using a deep learning approach

Principal Investigator: Dr Bruna Gomes Botelho Quintas
Approved Research ID: 63735
Approval date: January 11th 2021

Lay summary

Current class I recommendations endorsed by cardiac societies regarding aortic valve replacement in patients with aortic regurgitation without significant aortic root disease are exclusively indicated when it is graded as severe, whereas moderate aortic regurgitation is not regarded for aortic valve replacement. Left ventricular remodeling and clinical outcomes have been extensively studied in severe aortic regurgitation. However, the relationship between aortic regurgitation severity and left ventricular dysfunction severity remains unclear. Most natural history and epidemiologic studies were performed using traditional statistical analysis, failing to extrapolate results to the "real-world" setting of patients. On the other hand, artificial intelligence has shown promise in handling a patient's high-dimensionality. With this project we aim to predict development of symptoms and mortality in patients with chronic aortic regurgitation using two different artificial intelligence approaches: clustering analysis and hybrid deep learning models, following two work packages.

After screening for patients with aortic valve regurgitation, a high-dimensional dataset of input features varying from concomitant co-morbidities, genetic traits to imaging examinations will be considered. In work package 1, we aim at revealing novel patient group phenotypes using an unsupervised machine learning technique, by integrating different cardiac MRI features into a topological network. Also, considering the impact of concomitant cardiomyopathy, genetic background will be added to the topological network. We will analyze the network's geometry and define clinically relevant groups. Then, survival analysis upon the groups will be performed. Exploring the potentialities of hidden information from raw imaging data, in work package 2, models will be expanded to raw cardiac MRI data, based on a hybrid variational autoencoder model. Training and evaluation will use independent training, validation and testing sets. Python's high-level API Keras will be used. 

We estimate the duration of this project to be 24 Months. Considerable impact on public could be attained with this project, as it could define a more precise and individual decision line between conservative and interventional/surgical therapy in patients with chronic aortic valve regurgitation. Also, deepening the investigation on modeling time-to-event clinical outcomes from both raw imaging and structured data would be inestimable to any other field in cardiology pursing risk assessment. Moreover, we expect to generate novel variables by annotation of cardiac MRI files, which could be essential in future studies. Lastly, integration of genetic background data carries itself an unparallel approach to risk assessment in aortic valve disease.