ElectrocardiogramPRECISE-ECGNCT07179185
Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
A study testing AI-Assisted ECG Interpretation (AI-ECG) and Specialist ECG Interpretation Without AI in people with Electrocardiogram and Cardiovascular Abnormalities, run by Federal University of Minas Gerais.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Electrocardiogram, Cardiovascular Abnormalities
- What is being tested?
- AI-Assisted ECG Interpretation (AI-ECG) and Specialist ECG Interpretation Without AI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 710 participants
- Where
- No sites listed yet
- Study dates
- 1 Oct 2025to Nov 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- ECGs performed routinely by the Rede de Telemedicina de Minas Gerais (RTMG)
Who cannot join
- ECGs from patients younger than 18 years
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: PRECISE-ECG: Prospective Randomized Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
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
- Being tested
- AI-Assisted ECG Interpretation (AI-ECG), Specialist ECG Interpretation Without AI
- Keywords
- artificial intelligence, electrocardiography, diagnostic methods, telemedicine, normal electrocardiogram
- Medical subject headings
- Cardiovascular Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted ECG Interpretation (AI-ECG)Diagnostic test
Neural network-based AI software that analyzes ECG tracings and provides a classification as normal suggestion to the interpreting specialist.
Specialist ECG Interpretation Without AIDiagnostic test
Manual interpretation of ECGs by specialists without AI support, following standard diagnostic procedures
| Study group | Role | What they receive |
|---|---|---|
| Control - Specialist Interpretation Without AI | Active comparator | Specialist physicians interpret normal ECGs without the assistance of the AI-ECG tool. ECGs are routine tracings performed by the Rede de Telemedicina de Minas Gerais (RTMG). Final classification for study endpoints will be based on a panel review by three specialists. Receives: Diagnostic Test: Specialist ECG Interpretation Without AI |
| Specialist interpretation with AI assistance | Experimental | Specialist physicians interpret ECGs using the AI-ECG tool, which provides automated classification support indicating whether the ECG is normal or not. ECGs are routine tracings performed by RTMG. Final classification for study endpoints will be based on a panel review by three specialists. Receives: Diagnostic Test: AI-Assisted ECG Interpretation (AI-ECG) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: One week
Secondary outcomes (3)
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.
Measured: One week
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.
Measured: One week
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.
Measured: One week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Sep 2025
- Study start1 Oct 2025 (estimated)
- Primary completion5 Oct 2025 (estimated)
- Study completionNov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for ElectrocardiogramStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 710 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Federal University of Minas Gerais
- Sponsor type
- University, hospital or other
- Collaborators
- Uppsala University
- Responsible party
- Principal investigator, Antonio Luiz Pinho Ribeiro, Federal University of Minas Gerais
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07179185
- Sponsor's protocol ID
- 409604/2022-4
- Acronym
- PRECISE-ECG
- Status last verified
- Sep 2025
- First submitted
- 11 Sep 2025
- Last updated
- 22 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 39828428Ribeiro 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.
PubMed 31526573Ribeiro 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.
PubMed 32273514
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PRECISE-ECG: Prospective Randomized Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs
- ClinicalTrials.gov ID
- NCT07179185
- Acronym / sponsor ID
- PRECISE-ECG / 409604/2022-4
- Conditions
- Electrocardiogram, Cardiovascular Abnormalities
- Keywords
- artificial intelligence, electrocardiography, diagnostic methods, telemedicine, normal electrocardiogram
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 710 participants (estimated)
- Lead sponsor
- Federal University of Minas GeraisUniversity, hospital or other
- Collaborators
- Uppsala University
- Study start
- 1 Oct 2025 (estimated)
- Primary completion
- 5 Oct 2025 (estimated)
- Study completion
- Nov 2025 (estimated)
- First posted
- 17 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control - Specialist Interpretation Without AI | Active comparator | Diagnostic Test: Specialist ECG Interpretation Without AISpecialist physicians interpret normal ECGs without the assistance of the AI-ECG tool. ECGs are routine tracings performed by the Rede de Telemedicina de Minas Gerais (RTMG). Final classification for study endpoints will be based on a panel review by three specialists. |
| Specialist interpretation with AI assistance | Experimental | Diagnostic Test: AI-Assisted ECG Interpretation (AI-ECG)Specialist physicians interpret ECGs using the AI-ECG tool, which provides automated classification support indicating whether the ECG is normal or not. ECGs are routine tracings performed by RTMG. Final classification for study endpoints will be based on a panel review by three specialists. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- ECGs performed routinely by the Rede de Telemedicina de Minas Gerais (RTMG)
Key exclusion criteria (1 listed)
- ECGs from patients younger than 18 years
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 interpretation with AI assistance |
| Specialist ECG Interpretation Without AIManual interpretation of ECGs by specialists without AI support, following standard diagnostic procedures | Diagnostic test | Control - Specialist Interpretation Without AI |
Primary outcome
Precision (Positive Predictive Value) for detection of normal ECG tracings
Time frame: One week
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.
3 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2025.
- Background: PubMed 39828428Oliveira 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.
- Background: PubMed 31526573Ribeiro 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.
- Background: PubMed 32273514Ribeiro 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.
- Individual participant dataSharing planned: No
Related trials for Electrocardiogram
RecruitingObservational study
Clinical Performance and Usability of the Heart Rate and Impedance Respiration Monitor Cable
Monitoring, ECG
RecruitingObservational study
Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension
Pulmonary Hypertension, Pulmonary Arterial Hypertension
Not yet recruitingObservational study
AI-ECG Detection of Occlusion Myocardial Infarction at Emergency Department Triage (TRIACS)
Acute Coronary Syndromes, Chest Pain
Not yet recruitingObservational study
Cardiovascular Pre-participation Screening in Children and Adolescents
Sudden Cardiac Death, Cardiac Arrhythmias
Not yet recruitingObservational study
Multimodality Assessment of Myocardial Reperfusion After Primary Percutaneous Coronary Intervention
ST Elevation (STEMI) Myocardial Infarction, Microvascular Obstruction (MVO)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Assisted ECG Interpretation (AI-ECG) and Specialist ECG Interpretation Without AI in people with Electrocardiogram and Cardiovascular Abnormalities, run by Federal University of Minas Gerais.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Antonio Luiz P. Ribeiro, MD, PhD (55(31)3307-9201, alpr@ufmg.br), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Precision (Positive Predictive Value) for detection of normal ECG tracings (time frame: One week). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07179185, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2025.