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CADnet.html
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<!DOCTYPE HTML>
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<h1 style="letter-spacing: 7px; margin-bottom: 10px; font-size:5em; text-shadow:1px 1px 10px #fff, 3px 3px 3px #ccc">CRADLE</h1>
<p style="margin-bottom: 10px; font-size:1em">The Cardiovascular and Radiologic Deep Learning Environment</p>
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<li><a href="index.html#intro">Introduction</a></li>
<li><a href="index.html#first">Projects</a></li>
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<h2>Cardiac Amyloidosis Discovery</h2>
<h3>Changing the natural history of cardiac amyloidosis through earlier detection</h3>
</header>
<p>Cardiovascular disease remains the leading cause of death in the United States. Significant disparities exist
in cardiovascular disease by race, ethnicity, and socioeconomic status, including in the prevalence of risk
factors, the risk of specific cardiovascular conditions such as heart failure, and in access to diagnostic tools
such as echocardiography. Failure to diagnose cardiovascular disease represents a significant burden of
preventable cardiovascular disease and an opportunity to improve health and advance justice within the
healthcare system. </p>
<p>Transthyretin cardiac amyloidosis (ATTR-CA) is an underrecognized disorder that disproportionately affects
Hispanic and non-Hispanic Blacks who have both a higher prevalence of disease due to an underlying genetic
variant and worse survival after diagnosis compared to other populations. Although it is marked by ECG and
echocardiographic changes with decreasing ECG voltage and increasing LV wall thickness, ATTR-CA has historically
been diagnosed late in the disease course due to nonspecific signs and symptoms of early-stage disease. </p>
<span class="image story"><img src="CADnet/Graph.png" alt="" width="50%"/></span>
<p> In an effort to improve the early detection of cardiac amyloidosis when it is most treatable, we have
developed a deep learning model using the ECGs and echocardiograms of ~1,000 patients who have been tested for
cardiac amyloidosis. The core model uses a convolutional neural network to analyze the ECG waveform with tabular
LV measures from the echocardiogram fused into the model to yield a final model prediction.</p>
<span class="image main"><img src="CADnet/Performance_Figure.png" alt=""></span>
<p>We are currently carrying out a 100-patient prospective clinical study using this technology. By applying the
deep learning model to every patient at our medical center who has undergone an ECG and an echocardiogram, we
can identify the patients at highest risk for having undiagnosed cardiac amyloidosis. These patients are then
recruited for testing with the hope that this model will lead to earlier detection and improved outcomes.</p>
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<h2>Interested in starting your own career in ML for healthcare?</h2>
<p>Our white paper walks you through all the best resources we've found to learn about programming, machine learning, and medical AI research!</p>
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<p class="copyright">© 2022 CRADLE @ Columbia University
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