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Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs

NCT07179185 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 710

Last updated 2025-09-22

No results posted yet for this study

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

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

Sponsors & Collaborators

  • Uppsala University

    collaborator OTHER

  • Federal University of Minas Gerais

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2025-10-05
Completion
2025-11-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07179185 on ClinicalTrials.gov