Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-Assisted ECG Interpretation (AI-ECG), Specialist ECG Interpretation Without AI.
- Who it may be relevant to
- Registry conditions: Electrocardiogram, Cardiovascular Abnormalities. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
PRECISE-ECG: Prospective Randomized Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
Overview
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.
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
Primary outcome measures
- Precision (Positive Predictive Value) for detection of normal ECG tracings [Time frame: One week]
Secondary outcome measures (3)
- Sensitivity, Specificity, Negative Predictive Value, and F1 score for detection of normal ECG tracings [Time frame: One week]
- ECGs with major abnormalities incorrectly classified as normal [Time frame: One week]
- Time of analysis for normal cases (seconds per case) [Time frame: One week]
Eligibility criteria
Inclusion criteria
- ECGs performed routinely by the Rede de Telemedicina de Minas Gerais (RTMG)
Exclusion criteria
- ECGs from patients younger than 18 years
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Diagnostic
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- Oliveira CRA, Paixao GMM, Tostes VC, Gomes PR, Mendes MS, Paixao MC, Marcolino MS, Ribeiro ALP. Upscaling a regional telecardiology service to a nationwide coverage and beyond: the experience of the Telehealth Network of Minas Gerais. BMJ Glob Health. 2025 Jan 19;10(1):e016692. doi: 10.1136/bmjgh-2024-016692. PMID 39828428
- Ribeiro ALP, Paixao GMM, Gomes PR, Ribeiro MH, Ribeiro AH, Canazart JA, Oliveira DM, Ferreira MP, Lima EM, Moraes JL, Castro N, Ribeiro LB, Macfarlane PW. Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study. J Electrocardiol. 2019 Nov-Dec;57S:S75-S78. doi: 10.1016/j.jelectrocard.2019.09.008. Epub 2019 Sep 7. PMID 31526573
- Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020 Apr 9;11(1):1760. doi: 10.1038/s41467-020-15432-4. PMID 32273514
Identifiers
NCT: NCT07179185 · 409604/2022-4