NCT07179185

Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs

PRECISE-ECG: Prospective Randomized Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 710 (estimated)
• Sponsor: Federal University of Minas Gerais · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study will evaluate the performance of specialist physicians in interpreting normal electrocardiograms (ECGs) with and without the assistance of an artificial intelligence (AI) neural network. The primary aim is to determine whether AI support affects the rate of false-positive interpretations of normal tracings. Secondary aims include evaluating the time required for interpretation, the sensitivity for detecting abnormalities, and the effect on false positives in ECGs with major abnormalities according to the Minnesota Code system. All ECGs in the sample will be reviewed by a panel of three specialists, to determine the reference classification.

Conditions

• Electrocardiogram
• Cardiovascular Abnormalities

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	AI-Assisted ECG Interpretation (AI-ECG)	Neural network-based AI software that analyzes ECG tracings and provides a classification as normal suggestion to the interpreting specialist.
DIAGNOSTIC_TEST	Specialist ECG Interpretation Without AI	Manual interpretation of ECGs by specialists without AI support, following standard diagnostic procedures

Timeline

• Start date: 2025-10-01
• Primary completion: 2025-10-05
• Completion: 2025-11-01
• First posted: 2025-09-17
• Last updated: 2025-09-22