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A study in Indian adults using AI-assisted scans of the neck and heart blood vessels to improve the detection of heart disease

Radiomics based multimodal evaluation of atherosclerotic disease in Indian population: Correlation of Carotid Doppler IMT with CT Coronary Angiography and machine learning - driven plaque characterisation - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/07/113620
Enrollment
146
Registered
2026-07-07
Start date
Unknown
Completion date
Unknown
Last updated
2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: I708- Atherosclerosis of other arteries

Interventions

Intervention1: Nil: Nil

Sponsors

ANKITA DROLIA
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Indian adults aged 18 years or older Individuals undergoing CT coronary angiography and carotid Doppler ultrasonography on the same day as part of the study protocol Willing and able to provide written informed consent to participate in the study.

Exclusion criteria

Exclusion criteria: Individuals younger than 18 years Individuals who are unwilling or unable to provide written informed consent. Individuals with a known history of allergy or hypersensitivity to iodinated contrast media Individuals with incomplete or poor-quality CT coronary angiography or carotid Doppler ultrasound images that are unsuitable for analysis Individuals with a history of carotid artery surgery or carotid artery stenting that may interfere with carotid artery assessment.

Design outcomes

Primary

MeasureTime frame
1 To assess the relationship between carotid IMT on Doppler ultrasound and CAD severity on CT coronary angiography 2 To characterize plaque and utilize radionics for plaque analysis based on CT coronary angiography imagesTimepoint: Baseline or day 0 All study imaging including CT coronary angiography and carotid Doppler ultrasonography will be performed on the same day Outcome measures will be derived from these baseline images, with offline radiomic and AI-based image analysis performed subsequently

Secondary

MeasureTime frame
To evaluate radiomics-based ML models in differentiating significant, intermediate and non-significant CADTimepoint: baseline

Countries

India

Contacts

Public ContactDr Priya P S

kasturba medical college and hospital manipal MAHE

priya.ps@manipal.edu9895563264

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Aug 10, 2026