Atherosclerosis of Artery, Machine Learning, Metabolic Syndrome
Conditions
Keywords
Atherosclerosis of Artery, Metabolic Syndrome, Machine Learning
Brief summary
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.
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.
Interventions
None listed
Sponsors
Sultan Abdulhamid Han Training and Research Hospital, Istanbul, Turkey
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
Yes
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* 18 years or older * Having cranial CTA withdrawn * Having blood lipids, HbA1c, blood glucose, AST, ALT measured in 3 months before and 3 months after cranial CTA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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation of the machine learning model and manual interpretation | 1 day | Evaluation of the correlation of the presence of plaque in the carotid segments with manual interpretation in the model obtained by machine learning method |
Countries
Turkey (Türkiye)
Contacts
Primary ContactElif Yıldırım Ayaz, M.D.
Outcome results
None listed