Femoropopliteal Stenosis, Peripheral Artery Disease
Conditions
Brief summary
The femoropopliteal artery segment (FPAS) is one of the longest arteries in the human body, undergoing torsion, compression, flexion and extension due to lower limb movements. Endovascular surgery is considered to be the treatment of choice for the peripheral arterial disease, the results of which depend on the physiological forces on the arterial wall, the anatomy of the vessels and the characteristics of the lesions being treated. The atheromatous disease includes, in a simple way, 3 categories of plaques: calcified, fibrous, and lipidic. The study of these plaques and their differentiation in imaging and histology in the FPAS has already been the subject of research. To treat them, there are angioplasty balloons and stents with different designs and components, with different mechanical properties and different impregnated molecules. There is no non-invasive method (imaging) to accurately differentiate lesions along the FPAS. The analysis is performed from the preoperative CT scan, but there are high-resolution scanners that allow a quasi-histological analysis of the tissue. This microscanner can be used ex vivo. In the framework of a project, the learning algorithm was be créated (Convolutional Neural Networks) to automatically segment microscanner slices: after taking FPAS from amputated limbs, we correlated ex-vivo microscanner images of the arteries with their histology. The correlation was then performed manually between the microscanner images, and the histological sections obtained. the algorithm well be trained on these slices and validated its performance. The validation of the CT and microscanner concordance was the subject of scientific publications.
Detailed description
The aim of this study is to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT. The results of this study will provide initial data to evaluate the interest of a subsequent larger scale study to validate the diagnostic capabilities of automated segmentation
Interventions
PROCEDUREEndovascular surgery
routine endovascular surgery and FPAS harvesting from amputated limbs to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT
Sponsors
University Hospital, Strasbourg, France
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Male or female of legal age * Subject with a planned transfemoral amputation in the vascular surgery department of the Hôpitaux Universitaires de Strasbourg as standard care * Subject with a CT as part of standard care * Subject who has given his/her non-opposition to participate in the study
Exclusion criteria
\- Impossible to give the subject informed information (subject in emergency situation, difficulties in understanding)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Assessing the feasibility of histological segmentation of the superficial femoral artery on preoperative microscanner using artificial intelligence | 1 hour | Rate of slices (in %) for which segmentation is considered sufficient. The quality of segmentation will be assessed by the clinician using a Likert scale. Segmentation is considered sufficient if the scale is ≥ 3 and insufficient if it is \< 3 |
Countries
France
Contacts
Primary ContactSalomé KUNTZ, Doctor
Outcome results
None listed