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Updated: Aug 6, 2026
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
AI-ECG for Detecting Left Ventricular Systolic Dysfunction in Chagas Disease: Diagnostic and Prognostic Value
Clareci Silva Cardoso1, Kathryn Mangold2, José Luiz Padilha da Silva3
1Telehealth Center and Research Group in Epidemiology and Evaluation of New Technologies in Health, Federal University of São João del-Rei, Divinópolis, Brazil.
Insights
An artificial intelligence-enabled electrocardiogram (AI-ECG) accurately detects left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD). This AI-ECG tool also predicts mortality and new cases of LVSD, offering a potential alternative to NT-proBNP in resource-limited settings.
Area of Science:
- Cardiology
- Artificial Intelligence
- Public Health
Background:
- Left ventricular systolic dysfunction (LVSD) is a primary mortality predictor in Chagas disease (ChD).
- Diagnosis of LVSD typically requires cardiac imaging, which is often inaccessible in resource-limited areas.
- Affordable medications exist for LVSD, highlighting the need for accessible diagnostic tools.
Purpose of the Study:
- To evaluate an artificial intelligence-enabled electrocardiogram (AI-ECG) for detecting LVSD in Chagas disease patients.
- To assess the AI-ECG's ability to predict mortality and incident LVSD in this population.
- To compare the AI-ECG's performance against established biomarkers like NT-proBNP.
Main Methods:
- A fine-tuned AI-ECG model for LVSD was applied to the SaMi-Trop Brazilian cohort.
- Diagnostic performance for LVSD was compared with echocardiography and NT-proBNP.
- Prognostic performance for mortality and incident LVSD was evaluated using Cox and log-binomial models.
Main Results:
- AI-ECG demonstrated high accuracy (AUC: 0.89) for LVSD detection, comparable to NT-proBNP (AUC: 0.90).
- AI-ECG accurately predicted 2- and 9-year all-cause mortality and incident LVSD, with performance similar to NT-proBNP.
- AI-ECG showed potential as a substitute for NT-proBNP in mortality risk scores.
Conclusions:
- A fine-tuned AI-ECG model accurately detects LVSD and predicts long-term mortality and incident LVSD in Chagas disease.
- AI-ECG shows promise as a viable alternative to NT-proBNP, particularly in settings lacking NT-proBNP availability.
- The AI-ECG model's independent association with adverse outcomes supports its clinical utility in Chagas cardiomyopathy management.
Background:
Left ventricular systolic dysfunction (LVSD) is the main predictor of mortality in Chagas disease (ChD). Although LVSD can be treated with affordable medications, its diagnosis relies on cardiac imaging, which is often unavailable in resource-limited settings.
Objectives:
The objective of the study was to evaluate an artificial intelligence-enabled electrocardiogram (AI-ECG) for detecting LVSD and predicting mortality and incident LVSD in ChD.
Methods:
A previously developed AI-ECG LVSD model was fine-tuned in an external ChD sample and applied to SaMi-Trop, a Brazilian prospective ChD cohort. Diagnostic performance for LVSD, confirmed by echocardiography, was compared with N-terminal pro-B-type natriuretic peptide (NT-proBNP). Prognostic performance for all-cause mortality at 2 and 9 years was assessed using Cox models, and incident LVSD over 7 years using log-binomial models.
Results:
Among 1,304 participants, AI-ECG showed high accuracy for LVSD detection (area under the receiver operating characteristic curve: 0.89; 95% CI: 0.85-0.93), similar to NT-proBNP (area under the receiver operating characteristic curve: 0.90; 95% CI: 0.87-0.93; P = 0.52). Among 1,547 patients with Chagas cardiomyopathy, AI-ECG predicted all-cause mortality at 2 and 9 years with performance comparable to NT-proBNP (9-year C-index: 0.78; 95% CI: 0.74-0.82 vs 0.77; 95% CI: 0.74-0.79). AI-ECG could replace NT-proBNP in an established ChD mortality risk score with minor loss of accuracy. Incident LVSD occurred in 8.4% over 7.3 years, and AI-ECG predicted incident LVSD with performance similar to NT-proBNP.
Conclusions:
A fine-tuned AI-ECG model showed high accuracy for LVSD detection and was independently associated with long-term mortality and incident LVSD. Despite slightly lower performance than NT-proBNP, it may serve as a substitute in settings where NT-proBNP is unavailable. (Longitudinal Study of Patients With Chronic Chagas Cardiomyopathy in Brazil [SaMi_Trop Project] [SaMi-Trop]; NCT02646943).
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