FindTrial
Sign In

Retrospective study for validating Artificial Intelligence/ Deep learning-based models for left ventricular myocardial segmentation and scar quantification.

Automated scar quantification model using Artificial Intelligence (AI)/ Deep learning (DL) for myocardial ischemia and Left ventricular cardiomyopathy and its validation - None

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2021/08/035739
Enrollment
150
Registered
2021-08-17
Start date
Unknown
Completion date
Unknown
Last updated
2021-11-24

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

Conditions

Health Condition 1: I255- Ischemic cardiomyopathy

Interventions

None listed

Sponsors

Hearthealth Technologies Private Limited
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: All cases with delayed enhancement of myocardium

Exclusion criteria

Exclusion criteria: Absence of good quality imaging dataset

Design outcomes

Primary

MeasureTime frame
Proportion of patient with segmentation accuracy of OneCardio© is greater than or equal to 80% compared to manual technique and/or standard software. Proportion of patient with the difference between the fibrosis quantification determined by OneCardio© compared to the manual technique based on clinicianâ??s judgment is not more than 20%. Time for estimation of left-ventricle myocardial fibrosis using OneCardio© when compared to manual technique and/or available standard software. Timepoint: Baseline—

Secondary

MeasureTime frame
Secondary Endpoints Proportion of SAX images (in a patient dataset) that required rework (pixel-level correction) using OneCardio© and/or standard software.Timepoint: Baseline—

Countries

India

Contacts

Public ContactRavi Chivukula

Hearthealth Technologies Private Limited

arbind.gupta@gmail.com9845193233

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026