Carotid Atherosclerotic Plaque Load and Neck Circumference

Determination of Carotid Atherosclerotic Plaque Load and Neck Circumference in Cranial CT Angiography With Machine Learning Method and Their Relationship With Each Other

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.

Study Overview

• Status: Recruiting
• Conditions: Metabolic Syndrome; Machine Learning; Atherosclerosis of Artery

Detailed 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.

• Study Type: Observational
• Enrollment (Anticipated): 300

Contacts and Locations

Study Locations

• Turkey
  • İstanbul
    • Kadıköy, İstanbul, Turkey, 34668
      • Recruiting
      • Istanbul Medeniyet University Goztepe Prof. Dr. Suleyman Yalcın City Hospital
      • Contact: Elif Yıldırım Ayaz, M.D.; Phone Number: 05325148300

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 90 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

People having cranial CTA

Description

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• Having cranial CTA withdrawn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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation of the machine learning model and manual interpretation	Evaluation of the correlation of the presence of plaque in the carotid segments with manual interpretation in the model obtained by machine learning method	1 day

Collaborators and Investigators

• Sponsor: Sultan Abdulhamid Han Training and Research Hospital, Istanbul, Turkey

Study record dates

Study Major Dates

• Study Start (ACTUAL): September 8, 2021
• Primary Completion (ANTICIPATED): March 28, 2022
• Study Completion (ANTICIPATED): March 28, 2022

Study Registration Dates

• First Submitted: September 7, 2021
• First Submitted That Met QC Criteria: September 7, 2021
• First Posted (ACTUAL): September 10, 2021

Study Record Updates

• Last Update Posted (ACTUAL): September 17, 2021
• Last Update Submitted That Met QC Criteria: September 12, 2021
• Last Verified: September 1, 2021

More Information

Terms related to this study

• Keywords: Metabolic Syndrome; Machine Learning; Atherosclerosis of Artery
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Glucose Metabolism Disorders; Metabolic Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Pathological Conditions, Anatomical; Insulin Resistance; Hyperinsulinism; Metabolic Syndrome; Atherosclerosis; Plaque, Atherosclerotic
• Other Study ID Numbers: CAPNET

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No