Carotid Atherosclerotic Plaque Load and Neck Circumference

Sultan Abdulhamid Han Training and Research Hospital, Istanbul, Turkey

Status: Unknown

Conditions: Machine Learning; Atherosclerosis of Artery; Metabolic Syndrome

Study type: Observational

Funder types: Other

Identifiers

NCT05040958

CAPNET

Details and patient eligibility

About

The aim of this study is to establish a deep learning model to automatically detect the presence and scoring of carotid plaques in neck CTA images, and to determine whether this model is compatible with manual interpretations.

Full description

Modeling CTA images for carotid artery segments with deep learning method and automatic carotid plaque presence and scoring will be useful and beneficial in clinical practice. The aim of this study is to establish a deep learning model to automatically detect the presence and scoring of carotid plaques in neck CTA images, and to determine whether this model is compatible with manual interpretations.

Enrollment: 300 estimated patients

Sex: All

Ages: 18 to 90 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• 18 years or older
• Having cranial CTA withdrawn
• Having blood lipids, HbA1c, blood glucose, AST, ALT measured in 3 months before and 3 months after cranial CTA

Exclusion criteria

• Thyroid disease
• Having had neck surgery
• Use of corticosteroids for more than 6 months
• Presence of lymph nodes in the anterior neck
• Hypertrophy of neck muscles