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<html>
<head>
<title>CounteractIO · The Process</title>
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<meta name="Author" content="Andy Kamath" />
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<meta property="og:description" content="Noninvasive AI to detect colorectal cancer." />
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<ul class="right hide-on-med-and-down">
<li><a href="about.html">About</a></li>
<li><a href="process.html">Process</a></li>
<li><a href="https://medium.com/counteractio">Blog</a></li>
</ul>
<ul class="side-nav" id="mobile-demo">
<li><a href="about.html">About</a></li>
<li><a href="process.html">Process</a></li>
<li><a href="https://medium.com/counteractio">Blog</a></li>
</ul>
</div>
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<div class="main" style="height: 50%;background: url(img/bg1.jpg);background-size: cover;background-position: center center;background-attachment: fixed;">
<div style="height: 100%;width: 100%;background: rgba(0,0,0,0.5);color: #FFF">
<div class="element">
<div class="container">
<h1 class="title">The Process</h1>
<h5>Our patent pending methodology</h5>
</div>
</div>
</div>
</div>
<div id="team">
<div class="container">
<h4>What We Do</h4>
<br>
<p>We're trying to optimize noninvasive prescreen methodologies for colon cancer. Currently, the only existing prescreen tests for colorectal cancer count the amount of blood in the stool and make a diagnosis solely based on that metric. The fecal immunochemical test (FIT), the guiac fecal occult blood test (gFOBT), and other such blood count tests approved by the FDA are too generalized and therefore diagnose GI illnesses, but not always colorectal cancer itself.</p>
<center>
<a href="http://www.cancerresearchuk.org/sites/default/files/cstream-node/surv_5yr_stage_mw_bowel_1.png"><img style="max-width: 75%; min-width: 50%" src="http://www.cancerresearchuk.org/sites/default/files/cstream-node/surv_5yr_stage_mw_bowel_1.png" alt=""></a>
</center>
<h4>The Way We Do It</h4>
<br>
<p>
CounteractIO uses ensemble methods to maintain high levels of accuracy, specificity, and sensitivity on a prescreen test for colorectal cancer. The mechanism works through ensemble methods in which the FIT result, age, BMI, presence of diabetes, smoking occurrence, and history of polyps/lesions are inputs. The classification algorithms were selected based on their optimized ability to handle a broad range of inter-related inputs with a high accuracy and equally high, if not higher, sensitivity.
<br>
<br> Our results have turned out stunningly well. Here's an ROC Curve:
<br>
<br>
</p>
<div style="width: 100%; height: 60vh; background: url(img/curve1.png);background-size: contain;background-repeat: no-repeat; background-position: center center"></div>
<h4>Metrics:</h4>
<p>
<br> Specificity: 0.92
<br> Sensitivity: 0.89
<br> Precision: 0.90
<br> AUC: 0.95
<br> F1: 0.88
<br>
<br>
<br>
</p>
<h4>
Current Tests:
<br>
<br>
</h4>
<table style="width:100%">
<tr>
<th>Test</th>
<th>Description</th>
</tr>
<tr>
<td>Fecal Immunochemical Test</td>
<td><span class="red-text">Sensitivity: 74%</span>
<br>Specificity: 96%
<br>Cost: $15
<br>OTC
<br>5 minutes to get results
<br><span class="red-text">Too generalized - positive results don't necessarily mean CRC</span></td>
</tr>
<tr>
<td>Stool DNA Test</td>
<td>Sensitivity: 92%
<br>Specificity: 89%
<br><span class="red-text">Cost: $640</span>
<br><span class="red-text">Prescription-only</span>
<br><span class="red-text">Two weeks to get results</span>
<br><span class="red-text">Not for people with a history of lesions/polyps</span></td>
</tr>
<tr>
<td>Virtual CT Scan</td>
<td>Sensitivity: 84%
<br>Specificity: 88%
<br><span class="red-text">Cost: $400 - not covered by most insurance</span></td>
</tr>
<tr class="yellow">
<td>CounteractIO</td>
<td>Sensitivity: 89%
<br>Specificity: 92%
<br>Cost: $10
<br>No dietary restrictions</td>
</tr>
</table>
<br>
</div>
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