Ignite Creation Date:
2024-05-06 @ 3:02 PM
Last Modification Date:
2024-10-26 @ 1:42 PM
Study NCT ID:
NCT04503226
Status:
UNKNOWN
Last Update Posted:
2020-08-07
First Post:
2020-08-04
Brief Title:
Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair EVAR of Abdominal Aortic Aneurysm AAA
Sponsor:
Liverpool University Hospitals NHS Foundation Trust
Organization:
Liverpool University Hospitals NHS Foundation Trust
Study Overview
Official Title:
Deep Learning Applied to Plain Abdominal Radiographic Surveillance After Endovascular Aneurysm Repair EVAR of Abdominal Aortic Aneurysm AAA
Status:
UNKNOWN
Status Verified Date:
2019-08
Last Known Status:
ACTIVE_NOT_RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
DeepLearn
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None