<node id="627265">
  <nid>627265</nid>
  <type>event</type>
  <uid>
    <user id="27707"><![CDATA[27707]]></user>
  </uid>
  <created>1570469977</created>
  <changed>1570469977</changed>
  <title><![CDATA[PhD Defense by Daniel Whittingslow]]></title>
  <body><![CDATA[<p><strong>Daniel Whittingslow</strong></p>

<p><strong>Biomedical Engineering PhD&nbsp;Thesis&nbsp;Defense</strong></p>

<p>&nbsp;</p>

<p><strong>Date:</strong>&nbsp;Tuesday, October 22<sup>nd</sup>, 2019</p>

<p><strong>Time:&nbsp;</strong>11:00am - 1:00pm</p>

<p><strong>Location:&nbsp;</strong>Room 114, CODA Building, Tech Square, Georgia Tech</p>

<p><strong>Address:&nbsp;</strong>756 W Peachtree St NW, Atlanta, GA 30308</p>

<p>&nbsp;</p>

<p><strong>Advisor:</strong></p>

<p>Omer T. Inan, PhD</p>

<p>&nbsp;</p>

<p><strong>Committee Members:</strong></p>

<p>Rob Butera, PhD</p>

<p>Young-Hui Chang, PhD</p>

<p>Shelly Abramowicz, DMD, MPH, FACS</p>

<p>Sampath Prahalad, MD</p>

<p>&nbsp;</p>

<p><strong>Title:</strong>&nbsp;Anatomy of a Joint Sound &ndash; Using Joint Acoustic Emissions to Diagnose and Grade Musculoskeletal Disease and Injury</p>

<p>&nbsp;</p>

<p><strong>Abstract:</strong></p>

<p>Knee injuries and chronic disorders, such as arthritis, affect millions of Americans. Currently, diagnosis of these conditions relies primarily on imaging studies and physical examination by a health care professional. After diagnosis, there are few quantitative technologies available to provide feedback to patients regarding rehabilitation or efficacy of treatments. To address this need, I have developed a device capable of recording and analyzing a joint&rsquo;s acoustic emissions (AEs). In this work, I developed a human cadaver model of acute knee injury and found a consistent and repeatable variation of the observed AE patterns. This study helped us better understand the underlying anatomical contributors to these AE patterns. I then translated this technology into a clinical study and performed cross-sectional and longitudinal recordings of two groups of patients: a pediatric cohort with juvenile idiopathic arthritis (JIA), and an adult cohort with acute, traumatic injuries. With those patients recorded, I computed features based on their low-level acoustic emission signals. Machine learning algorithms fused those feature sets into an easily interpretable joint health metric for use in clinical diagnosis and decision making.</p>

<p>&nbsp;</p>
]]></body>
  <field_summary_sentence>
    <item>
      <value><![CDATA[Anatomy of a Joint Sound – Using Joint Acoustic Emissions to Diagnose and Grade Musculoskeletal Disease and Injury]]></value>
    </item>
  </field_summary_sentence>
  <field_summary>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_summary>
  <field_time>
    <item>
      <value><![CDATA[2019-10-22T12:00:00-04:00]]></value>
      <value2><![CDATA[2019-10-22T14:00:00-04:00]]></value2>
      <rrule><![CDATA[]]></rrule>
      <timezone><![CDATA[America/New_York]]></timezone>
    </item>
  </field_time>
  <field_fee>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_fee>
  <field_extras>
      </field_extras>
  <field_audience>
          <item>
        <value><![CDATA[Faculty/Staff]]></value>
      </item>
          <item>
        <value><![CDATA[Public]]></value>
      </item>
          <item>
        <value><![CDATA[Graduate students]]></value>
      </item>
          <item>
        <value><![CDATA[Undergraduate students]]></value>
      </item>
      </field_audience>
  <field_media>
      </field_media>
  <field_contact>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_contact>
  <field_location>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_location>
  <field_sidebar>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_sidebar>
  <field_phone>
    <item>
      <value><![CDATA[]]></value>
    </item>
  </field_phone>
  <field_url>
    <item>
      <url><![CDATA[]]></url>
      <title><![CDATA[]]></title>
            <attributes><![CDATA[]]></attributes>
    </item>
  </field_url>
  <field_email>
    <item>
      <email><![CDATA[]]></email>
    </item>
  </field_email>
  <field_boilerplate>
    <item>
      <nid><![CDATA[]]></nid>
    </item>
  </field_boilerplate>
  <links_related>
      </links_related>
  <files>
      </files>
  <og_groups>
          <item>221981</item>
      </og_groups>
  <og_groups_both>
          <item><![CDATA[Graduate Studies]]></item>
      </og_groups_both>
  <field_categories>
          <item>
        <tid>1788</tid>
        <value><![CDATA[Other/Miscellaneous]]></value>
      </item>
      </field_categories>
  <field_keywords>
          <item>
        <tid>100811</tid>
        <value><![CDATA[Phd Defense]]></value>
      </item>
      </field_keywords>
  <field_userdata><![CDATA[]]></field_userdata>
</node>
