Deep Learning for EVAR surveillance
Research type
Research Study
Full title
Deep learning applied to plain abdominal radiographic surveillance after Endovascular Aneurysm Repair (EVAR) of Abdominal Aortic Aneurysm (AAA).
IRAS ID
259034
Contact name
SR Vallabhaneni
Contact email
Sponsor organisation
Royal Liverpool and Broadgreen University Hospitals NHS Trust
Clinicaltrials.gov Identifier
5851, RD&I "Intent to Sponsor" at Royal Liverpool University Hospital
Duration of Study in the UK
1 years, 11 months, 31 days
Research summary
Abdominal aortic aneurysm (AAA) is a condition in which the largest blood vessel in the body becomes weak and forms a bulge. If it becomes large enough, the AAA can burst often leading to death. We therefore repair AAAs before they burst. Endovascular aneurysm repair (EVAR) is a standard treatment in the majority of patients. It is a keyhole technique that reinforces the aorta with a synthetic tube called a “stent-graft”.
EVAR is a safer option in the short-term compared to traditional open surgery. However in the long term, 1 in 5 patients require further surgery to correct problems developing with the stent-graft such as loss of position and integrity of the stent-graft. Therefore, patients are followed up for life after EVAR with scans performed, usually on an annual basis, to look for signs of a failing stent-graft.
Stent-grafts are visible on x-rays of the belly and by comparing series of images taken over time, it is possible to diagnose a myriad of stent-graft problems including loss of positioning, disintegration of the stent-graft material as well as stent-graft distortion. But these changes can be subtle and difficult to spot, even to the trained human eye. As a result, patients undergo more detailed scans that unfortunately carry a risk of kidney damage and radiation-induced cancer.
Our study will explore the use of artificial intelligence in interpreting series of anonymised x-rays to identify features of a failing stent-graft. A deep-learning algorithm will be applied to post-EVAR x-rays that have been performed as part of standard care at our institution over the last 13 years.
We wish to examine if deep learning program can be as good as or even better than human interpretation.
REC name
North West - Liverpool Central Research Ethics Committee
REC reference
19/NW/0311
Date of REC Opinion
5 Jul 2019
REC opinion
Further Information Favourable Opinion