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Development of AI model For Analysis of Chronic Kidney Disease Using Kidney Ultrasound Images

AI-Based Multimodal Estimation of eGFR Using Kidney Ultrasound and Clinical Parameters - Nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/05/110765
Enrollment
10000
Registered
2026-05-21
Start date
Unknown
Completion date
Unknown
Last updated
2026-09-14

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

Conditions

Health Condition 1: N189- Chronic kidney disease, unspecified

Interventions

Intervention1: Nil: Nil

Sponsors

Translational AI for Networked Universal Healthcare
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Completion of a minimum of one kidney ultrasound examination within the study period.

Exclusion criteria

Exclusion criteria: Subjects who have undergone kidney transplants and subjects who have undergone any surgery before an year of study.

Design outcomes

Primary

MeasureTime frame
To Develop and Validate an AI-Based Multimodal Model for Estimation of eGFR using Kidney Ultrasound Imaging Features and Available Clinical ParametersTimepoint: Baseline, 4 weeks, 8 weeks—

Secondary

MeasureTime frame
To Classify CKD Stages Based on Predicted eGFR and Identification of Early CKD.Timepoint: 5 years—

Countries

India

Contacts

Public ContactProf Phaneendra K Yalavarthy

TRANSLATIONAL AI FOR NETWORKED UNIVERSAL HEALTHCARE FOUNDATION

phaneendra.yalavarthy@tanuh.ai08022934106

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Sep 19, 2026