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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="_x0000_i1037" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation1.png" alt="Dissertation Defense Announcement at the Cullen College of Engineering"></span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">AI Approaches for Feature Extraction in Multimodal Biomedical Data</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">David Anderson Lloyd</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">December 3, 2024; 11:00 AM - 01:00 PM (CST)<br>
Location: SERC 2013<br>
Teams: <a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_NzMxY2YzN2YtMGQwOS00MWQwLWI0NzMtNzA5Y2ExM2M4ZWFi*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*223c558624-4304-42b8-a1f3-6fc12ac8038a*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!HioiaVyqcsZB52Rx5LCSZHkSp6jbg1-4lKTGBlKomQWsV2eKtDXCdi-rcUaNSdaUMAyxi0tPCOgZroeq-RexcYaGUjg$">
<span style="color:blue">https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzMxY2YzN2YtMGQwOS00MWQwLWI0NzMtNzA5Y2ExM2M4ZWFi%40thread.v2/0?context=%7b%22Tid%22%3a%22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259%22%2c%22Oid%22%3a%223c558624-4304-42b8-a1f3-6fc12ac8038a%22%7d</span></a><o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><br>
Metin Akay, Ph.D.<o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><br>
Mario Romero-Ortega Ph.D. | Scott Smith, Ph.D. | Yasemin Akay, Ph.D. | Tianfu Wu, Ph.D. | Yuncheng Du, Ph.D.<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Abstract</span></b><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none">Artificial intelligence (AI) has myriad applications for biomedical engineering and healthcare. In particular, with the diverse modalities and
high resolution of physiological data, the potential for data-driven approaches to elucidate meaningful insights is enormous. However, this enormity and profundity of this data is also a hindrance. AI approaches can fail to learn due to "feature dilution",
where the overload of data points and spurious or noisy features prevent the model from successfully aligning to and converging on semantic or predictive features in the dataset. Traditional methods of feature extraction either rely on manual physiological
inference or leverage pre-trained AI models for well-characterized applications. We posit that AI itself can be leveraged to augment feature identification through the exploitation of learned representations and intelligent processes. To address this problem,
we present three approaches for the application of artificial intelligence to the task of feature extraction in three different models of biomedical data: 1. AxoDetect (AD), a complete image-to-model software pipeline for
<i>in silico</i> nerve stimulation using computer vision (CV); 2. Probability Gradient Thresholding (PGT), a deep learning-based dynamic time window extraction method for the identification of neuronal compound action potential (CAP) spikes from neuronal recordings;
and 3. Latent Semantic Atom Analysis (LSAA), a hybrid approach using Matching Pursuit (MP) and DeepSets (DS) to learn patient-specific representations of atherosclerosis in the coronary arteries from human heart sound data. AxoDetect excels at segmenting nerves
without requiring labeled datasets, segmenting between ten to one hundred times faster than similar deep learning methods. The models which are functionalized and constructed by the software have statistically indistinguishable results when compared to the
gold standard modeling methods (Wilcoxon followed by MWU with Bonferroni correction). A combined RNN-LSTM approach to PGT was able to achieve over 95% point-wise accuracy on the testing set in identifying a data point's belonging to a neuronal spike on
<i>in silico</i> recordings. Finally, using LSAA we can not only predict presence or absence of cardiac stent with high accuracy (88% on the test set, 94% on the full dataset), we can isolate and cluster semantic groups of atoms representing cardiac occlusion.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black;mso-ligatures:none"><img border="0" width="600" height="82" style="width:6.25in;height:.8541in" id="_x0000_i1038" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation2.png" alt="Engineered For What's Next"></span><span style="mso-ligatures:none"><o:p></o:p></span></p>
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