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AI-ECG Structural Heart Disease - Grupo 2 PubMed edit
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Review Info

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Review Title:
AI-ECG Structural Heart Disease - Grupo 2 PubMed
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Review Type:
Systematic Review
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Review Domain:
Biomedical
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Description:
Systematic review and meta-analysis of diagnostic test accuracy studies evaluating artificial intelligence-enhanced electrocardiography for detecting structural heart disease.

Data Summary

Imported References
3,552
Total Duplicates
38
Unresolved
0
Resolved 19
Not Duplicate 6
Deleted 13

Review Members

NameEmailRoleStatus
WSMr. Wagner Santos
wagner.redes@gmail.com OwnerActive

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3,539 Articles to screen

Screening Summary

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Imported datasets

PubMed CSV
3,552

Database status

SQLite records ready
AI screening rows pending import

Articles to screen

#1Artificial intelligence electrocardiography for structural heart disease detection
#2Deep learning ECG screening for left ventricular dysfunction
#3Wearable ECG and machine learning for cardiac phenotyping

Article details

Artificial intelligence electrocardiography for structural heart disease detection

This diagnostic test accuracy study evaluates artificial intelligence-enhanced electrocardiography for detecting structural heart disease in adult patients.

ECGArtificial intelligenceDiagnostic accuracyStructural heart disease

Authors Silva M; Santos W; Oliveira R
Journal Journal of Digital Cardiology
Year 2026
DOI 10.1000/brayyan.001

Screening criteria

Inclusion
Exclusion
Include studies when:
Population includes adults or clinical cohorts evaluated with electrocardiography; intervention/index test includes artificial intelligence, machine learning, deep learning, neural network, or automated ECG interpretation; target condition includes structural heart disease, cardiomyopathy, valvular disease, ventricular dysfunction, hypertrophy, or related phenotype; study reports diagnostic performance, validation, screening accuracy, or clinically relevant prediction metrics.
Exclude studies when:
No ECG data, no AI method, animal-only study, non-diagnostic editorial/commentary, unrelated outcome, or no original validation data.
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Data Extraction

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Risk of Bias

Prepare QUADAS-2 or custom bias domains for diagnostic test accuracy studies.

Practical MVP readiness

1. ImportCSV parser exists. Production import must use a persistent database, not startup loading.
2. ScreeningArticle-by-article review is the next operational step after data persistence.
3. Conflicts A vs BConflict metadata exists and needs a live resolution workflow.
4. PRISMAFlow counts will come from imported records and final decisions.
5. KappaAgreement metrics require persisted A/B decisions.
6. ExportExport should include metadata, decisions, conflicts and final labels.

Operational screens

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PRISMA flow

Identified - duplicates - screened - excluded - full text - included.

Identified3,539
IncludedTBD

Kappa metrics

Agreement and Cohen's Kappa become active when A/B reviewer decisions are persisted.

K = TBD

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Auth readiness

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StatusPlanned
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