A Deep Learning model for the classification of cardiac amyloidosis among patients with Left Ventricular Hypertrophy.
Our research project - "A Deep Learning model for the early detection of cardiac amyloidosis among patients with Left Ventricular Hypertrophy", aims to develop a deep learning based algorithm for classification of various diseases that cause Left Ventricular Hypertrophy - with an emphasis on cardiac amyloidosis among those diseases.
Left Ventricular Hypertrophy is a state in which the heart muscle (myocardium) is over-thickened, and as a result it's filling capacity is reduced.
The above described state can arise from a number of diseases, one of them is cardiac amyloidosis.
Cardiac amyloidosis is an underdiagnosed, potentially reversible disease, that cause 'Restrictive Cardiomyopathy' which leads to progressive Heart Failure with life threatening arrhythmias. The disease arise from abnormal deposition of proteins in a number of tissues, including the heart.
As for today, diagnosis of cardiac amyloidosis is given after a series of tests, including imaging studies, blood tests, scintigraphy and heart biopsy. All of the above makes the journey of the patient towards diagnosis long, expensive and filled with uncertainty.
Given the underdiagnosis and significant morbidity of cardiac amyloidosis, combined with the availability of treatment with disease-modyfing agents, highlights the importance of early diagnosis and treatment.
Several studies has been conducted in order to built a prediction tool for cardiac amyloidosis. Most of them were based on clinical data alone.
We strongly believe that by applying computational learning methods, We'll be able to built a robust classification tool, that we'll allow us to predict cardiac amyloidosis as early as possible, and to start disease modifying treatment before the development of progressive heart failure.
In order to do so, we wish to conduct a retrospective study on UK Biobank patients diagnosed with Left Ventricular Hypertrophy based on echocardiographic/electrogram criteria, in order to generate a prediction model for classification of cardiac amyloidosis. Such classification model will enable us to diagnose the disease at an early state, and provide disease-modyfing medication for slowing its progression.
The requested data is a set of several clinical evaluation tools for the patient with heart disease - cardiac MRI (Magnetic Resonance Imaging), Echocardiography, Electrogram and clinical data.
Apllying image processing computational methods on the above will allow us to extract meaningful features for prediction. Combining those with clinical data, will enable us to train state of the art Artificial Intelligence models based on neural networks, that hopefully will be able to classify correctly and thus predict, cardiac amyloidosis in patients with Left Ventricular Hypertrophy.