Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA)

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Brief Title

Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA)

Official Title

Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA)

Brief Summary

      Deep learning applied to plain abdominal radiographic surveillance after Endovascular
      Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA).
    

Detailed Description

      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.
    


Study Type

Observational


Primary Outcome

Diagnostic Accuracy


Condition

Abdominal Aortic Aneurysm



Publications

* Includes publications given by the data provider as well as publications identified by National Clinical Trials Identifier (NCT ID) in Medline.

Recruitment Information



Estimated Enrollment

800

Start Date

October 1, 2019

Completion Date

December 31, 2020

Primary Completion Date

October 15, 2020

Eligibility Criteria

        Inclusion Criteria:

          -  Patients who have undergone EVAR at the Royal Liverpool University Hospital between
             2005 and 2013.

          -  Patients who were treated for standard infra-renal AAAs.

          -  Patients who are on our post-operative surveillance programme and have had 5 plain
             abdominal radiographs to date.

        Exclusion Criteria:

          -  None
      

Gender

All

Ages

N/A - N/A

Accepts Healthy Volunteers

No

Contacts

Srinivasa Rao Vallabhaneni, MD, FRCS, , 

Location Countries

United Kingdom

Location Countries

United Kingdom

Administrative Informations


NCT ID

NCT04503226

Organization ID

5851


Responsible Party

Sponsor

Study Sponsor

Liverpool University Hospitals NHS Foundation Trust


Study Sponsor

Srinivasa Rao Vallabhaneni, MD, FRCS, Principal Investigator, Royal Liverpool University Hospital NH STrust


Verification Date

August 2019