<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04503226</url>
  </required_header>
  <id_info>
    <org_study_id>5851</org_study_id>
    <nct_id>NCT04503226</nct_id>
  </id_info>
  <brief_title>Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA)</brief_title>
  <acronym>DeepLearn</acronym>
  <official_title>Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Liverpool University Hospitals NHS Foundation Trust</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Liverpool University Hospitals NHS Foundation Trust</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Deep learning applied to plain abdominal radiographic surveillance after Endovascular&#xD;
      Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA).&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Abdominal aortic aneurysm (AAA) is a condition in which the abdominal aorta, a large artery,&#xD;
      dilates gradually, secondary to a degenerative process within its wall. This can lead to&#xD;
      rupture of the weakened wall with subsequent exsanguination into the abdomen. This scenario&#xD;
      is usually fatal. The diameter of the aneurysm positively correlates with the risk of&#xD;
      rupture. Aneurysm size is therefore the primary determinant when considering whether or not&#xD;
      to electively repair AAAs.&#xD;
&#xD;
      Endovascular aneurysm repair (EVAR) has become the standard treatment for AAAs in the vast&#xD;
      majority of patients. It is a minimally invasive technique that aims to exclude the aneurysm&#xD;
      from the circulation by placement of a synthetic &quot;stent-graft&quot; within the aortic lumen.&#xD;
      Metallic barbs as well as radial force maintain stent-graft position in non-aneurysmal aorta&#xD;
      above the aneurysm as well as in the iliac arteries below the aneurysm.&#xD;
&#xD;
      Level 1 evidence has consistently demonstrated improved perioperative survival with EVAR as&#xD;
      compared to traditional open surgery. However, there are concerns regarding the long-term&#xD;
      durability of EVAR stent-grafts, with 1 in 5 patients requiring further surgery to the&#xD;
      aneurysm in the 5 years after the operation. This is often due to failure of the position and&#xD;
      integrity of the stent-graft. Therefore, standard international practice is to keep patients&#xD;
      are life-long surveillance after EVAR. This is usually in the form of plain radiographs in&#xD;
      combination with either computerised tomography (CT) or duplex ultrasound scans, all&#xD;
      performed on an annual basis.&#xD;
&#xD;
      Stent-grafts are visible on plain radiographs of the abdomen and by comparing series of&#xD;
      images taken over time, it is possible to diagnose a myriad of stent-graft problems including&#xD;
      migration, disintegration and distortion. But these changes can be subtle on plain&#xD;
      radiographs and difficult to spot, even to the most trained human eye. As a result, patients&#xD;
      undergo more detailed scans that unfortunately carry a risk of nephrotoxicity and&#xD;
      radiation-induced malignancy.&#xD;
&#xD;
      The aim of our research is to improve the diagnostic potential of plain radiographs by&#xD;
      applying modern deep learning computer algorithms for interpretation.&#xD;
&#xD;
      Artificial intelligence (AI) in the form of deep learning has shown great success in recent&#xD;
      years on numerous challenging problems. The success of deep learning is largely underpinned&#xD;
      by advances in powerful graphics processing units (GPUs). GPUs enable us to speed up training&#xD;
      algorithms by orders of magnitude, bringing run-times of weeks down to days.&#xD;
&#xD;
      Our study will explore the use of artificial intelligence in interpreting series of&#xD;
      anonymised plain radiographs to identify features of a failing stent-graft.&#xD;
&#xD;
      A deep-learning algorithm will be applied to post-EVAR plain radiographs that have already&#xD;
      been performed at our institution in England over the last 10 years. We will then compare the&#xD;
      effectiveness of the machine in identifying stent-graft related problems to the known&#xD;
      outcomes identified by human interpretation previously.&#xD;
&#xD;
      This project will rely on recent advances in deep learning techniques. It is expected that&#xD;
      deep learning will bring good performance for EVAR surveillance in line with its successful&#xD;
      application in domains such as the recognition of digits, Chinese characters, and traffic&#xD;
      signs where computers have produced better accuracy than humans.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Active, not recruiting</last_known_status>
  <start_date type="Actual">October 1, 2019</start_date>
  <completion_date type="Anticipated">December 31, 2020</completion_date>
  <primary_completion_date type="Anticipated">October 15, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Diagnostic Accuracy</measure>
    <time_frame>1-10 years</time_frame>
    <description>The diagnostic accuracy of deep learning based algorithm compared to trained human interpretation in the detection of stent graft migration, disintegration and distortion on plain x-rays after EVAR.</description>
  </primary_outcome>
  <enrollment type="Anticipated">800</enrollment>
  <condition>Abdominal Aortic Aneurysm</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Patients (approximately n = 800) who have undergone EVAR at the Royal Liverpool University&#xD;
        Hospital between 2005 and 2013.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients who have undergone EVAR at the Royal Liverpool University Hospital between&#xD;
             2005 and 2013.&#xD;
&#xD;
          -  Patients who were treated for standard infra-renal AAAs.&#xD;
&#xD;
          -  Patients who are on our post-operative surveillance programme and have had 5 plain&#xD;
             abdominal radiographs to date.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  None&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Srinivasa Rao Vallabhaneni, MD, FRCS</last_name>
    <role>Principal Investigator</role>
    <affiliation>Royal Liverpool University Hospital NH STrust</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of Liverpool</name>
      <address>
        <city>Liverpool</city>
        <state>Merseyside</state>
        <zip>L7 8TX</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>August 2019</verification_date>
  <study_first_submitted>August 4, 2020</study_first_submitted>
  <study_first_submitted_qc>August 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">August 7, 2020</study_first_posted>
  <last_update_submitted>August 4, 2020</last_update_submitted>
  <last_update_submitted_qc>August 4, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">August 7, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Endovascular Aneurysm Repair</keyword>
  <keyword>Surveillance</keyword>
  <keyword>Device migration</keyword>
  <keyword>Device disintegration</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Aneurysm</mesh_term>
    <mesh_term>Aortic Aneurysm</mesh_term>
    <mesh_term>Aortic Aneurysm, Abdominal</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04503226
org study id: 5851
nct id: NCT04503226
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Deep learning applied to plain abdominal radiographic surveillance after Endovascular
      Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA).
Abdominal aortic aneurysm (AAA) is a condition in which the abdominal aorta, a large artery,
      dilates gradually, secondary to a degenerative process within its wall. This can lead to
      rupture of the weakened wall with subsequent exsanguination into the abdomen. This scenario
      is usually fatal. The diameter of the aneurysm positively correlates with the risk of
      rupture. Aneurysm size is therefore the primary determinant when considering whether or not
      to electively repair AAAs.

      Endovascular aneurysm repair (EVAR) has become the standard treatment for AAAs in the vast
      majority of patients. It is a minimally invasive technique that aims to exclude the aneurysm
      from the circulation by placement of a synthetic "stent-graft" within the aortic lumen.
      Metallic barbs as well as radial force maintain stent-graft position in non-aneurysmal aorta
      above the aneurysm as well as in the iliac arteries below the aneurysm.

      Level 1 evidence has consistently demonstrated improved perioperative survival with EVAR as
      compared to traditional open surgery. However, there are concerns regarding the long-term
      durability of EVAR stent-grafts, with 1 in 5 patients requiring further surgery to the
      aneurysm in the 5 years after the operation. This is often due to failure of the position and
      integrity of the stent-graft. Therefore, standard international practice is to keep patients
      are life-long surveillance after EVAR. This is usually in the form of plain radiographs in
      combination with either computerised tomography (CT) or duplex ultrasound scans, all
      performed on an annual basis.

      Stent-grafts are visible on plain radiographs of the abdomen and by comparing series of
      images taken over time, it is possible to diagnose a myriad of stent-graft problems including
      migration, disintegration and distortion. But these changes can be subtle on plain
      radiographs and difficult to spot, even to the most trained human eye. As a result, patients
      undergo more detailed scans that unfortunately carry a risk of nephrotoxicity and
      radiation-induced malignancy.

      The aim of our research is to improve the diagnostic potential of plain radiographs by
      applying modern deep learning computer algorithms for interpretation.

      Artificial intelligence (AI) in the form of deep learning has shown great success in recent
      years on numerous challenging problems. The success of deep learning is largely underpinned
      by advances in powerful graphics processing units (GPUs). GPUs enable us to speed up training
      algorithms by orders of magnitude, bringing run-times of weeks down to days.

      Our study will explore the use of artificial intelligence in interpreting series of
      anonymised plain radiographs to identify features of a failing stent-graft.

      A deep-learning algorithm will be applied to post-EVAR plain radiographs that have already
      been performed at our institution in England over the last 10 years. We will then compare the
      effectiveness of the machine in identifying stent-graft related problems to the known
      outcomes identified by human interpretation previously.

      This project will rely on recent advances in deep learning techniques. It is expected that
      deep learning will bring good performance for EVAR surveillance in line with its successful
      application in domains such as the recognition of digits, Chinese characters, and traffic
      signs where computers have produced better accuracy than humans.
observational model: Case-Only
time perspective: Retrospective
measure: Diagnostic Accuracy
time frame: 1-10 years
description: The diagnostic accuracy of deep learning based algorithm compared to trained human interpretation in the detection of stent graft migration, disintegration and distortion on plain x-rays after EVAR.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Srinivasa Rao Vallabhaneni, MD, FRCS
role: Principal Investigator
affiliation: Royal Liverpool University Hospital NH STrust
facility: 
country: United Kingdom
responsible party type: Sponsor
mesh term: Aneurysm
mesh term: Aortic Aneurysm
mesh term: Aortic Aneurysm, Abdominal