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

Abdominal Aortic Aneurysm Clinical Trial

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Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04503226
• Other study ID #: 5851
• Status: Active, not recruiting
• Start date: October 1, 2019
• Est. completion date: December 31, 2020

Study information

• Verified date: August 2019
• Source: Liverpool University Hospitals NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 800
• Est. completion date: December 31, 2020
• Est. primary completion date: October 15, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Abdominal Aortic Aneurysm
• Aneurysm
• Aortic Aneurysm
• Aortic Aneurysm, Abdominal

Locations

Country	Name	City	State
United Kingdom	University of Liverpool	Liverpool	Merseyside

Sponsors (1)

Lead Sponsor	Collaborator
Liverpool University Hospitals NHS Foundation Trust

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic Accuracy	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.	1-10 years