Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorFederal University of Minas Gerais
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
ECGs performed routinely by the Rede de Telemedicina de Minas Gerais (RTMG)
Disqualifiers
ECGs from patients younger than 18 years
Trial design
Design model
Parallel
Treatments tested in this trial
AI-Assisted ECG Interpretation (AI-ECG)
Diagnostic testNeural network-based AI software that analyzes ECG tracings and provides a classification as normal suggestion to the interpreting specialist.
Specialist ECG Interpretation Without AI
Diagnostic testManual interpretation of ECGs by specialists without AI support, following standard diagnostic procedures
Treatment groups
710 Participants
are divided into 2 treatment groupsGroup A: Control - Specialist Interpretation Without AIActive comparator 1 intervention
Group B: Specialist interpretation with AI assistanceExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
Precision (Positive Predictive Value) for detection of normal ECG tracings
Precision (Positive Predictive Value) of detecting normal ECG by the physician or physician+model compared against the reference standard defined by a panel of three specialists. Precision (Positive Predictive Value) is defined by the number of true positive normal cases divided by all positive predictions.
Time frame
One weekSecondary outcomes
1
Sensitivity, Specificity, Negative Predictive Value, and F1 score for detection of normal ECG tracings
Accuracy evaluated by Sensitivity, Specificity, Negative Predictive Value, and F1 score of normal ECGs correctly identified by the physician or physician+model, in relation to a reference standard defined by a panel of three specialists.
Time frame
One week2
ECGs with major abnormalities incorrectly classified as normal
Ratio of ECGs with major abnormalities according to the Minnesota Code system among those incorrectly classified as normal by the physician or physician+model, in relation to a reference standard defined by a panel of three specialists.
Time frame
One week3
Time of analysis for normal cases (seconds per case)
Time required by the physician, or physician+model, to interpret normal ECGs, measured in seconds per case; the reference standard of normal cases defined by a panel of three specialists.
Time frame
One weekLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Federal University of Minas Gerais
Lead sponsor
Uppsala University
Collaborator
Source ClinicalTrials.gov
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