Histological Segmentation of the Superficial Femoral Artery From Microscan to CT Using Artificial Intelligence

NCT ID: NCT06260488

Last Updated: 2025-04-25

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 20 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-03-15
• Study Completion Date: 2025-08-15

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

Conditions

Peripheral Artery Disease; Femoropopliteal Stenosis

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

transfemoral amputation

Subject with a planned transfemoral amputation in the vascular surgery department of the Hôpitaux Universitaires de Strasbourg as standard care

Group Type: OTHER

Endovascular surgery

Intervention Type: PROCEDURE

routine endovascular surgery and FPAS harvesting from amputated limbs to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT

Interventions

Endovascular surgery

routine endovascular surgery and FPAS harvesting from amputated limbs to evaluate the technical feasibility of histological segmentation by the FPAS algorithm from CT

Intervention Type: PROCEDURE

Eligibility Criteria

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)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University Hospital, Strasbourg, France

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hôpitaux Universitaire de Strasbourg

Strasbourg, Bas-Rhin, France

Site Status: RECRUITING

Countries

France

Central Contacts

Salomé KUNTZ, Doctor

Role: CONTACT

salome.kuntz@chru-strasbourg.fr

+31 3 69 55 01 98

Facility Contacts

Salomé KUNTZ, Doctor

Role: primary

salome.kuntz@chru-strasbourg.fr

+31 3 69 55 01 98

Other Identifiers

8925

Identifier Type: -

Identifier Source: org_study_id