Screening for Chagas disease from the electrocardiogram using a deep neural network.

• Main Authors: Carl Jidling (Author), Daniel Gedon (Author), Thomas B Schön (Author), Claudia Di Lorenzo Oliveira (Author), Clareci Silva Cardoso (Author), Ariela Mota Ferreira (Author), Luana Giatti (Author), Sandhi Maria Barreto (Author), Ester C Sabino (Author), Antonio L P Ribeiro (Author), Antônio H Ribeiro (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2023-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

<h4>Background</h4>Worldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). It is a neglected disease that can lead to severe heart conditions in its chronic phase. While early treatment can avoid complications, the early-stage detection rate is low. We explore the use of deep neural networks to detect ChD from electrocardiograms (ECGs) to aid in the early detection of the disease.<h4>Methods</h4>We employ a convolutional neural network model that uses 12-lead ECG data to compute the probability of a ChD diagnosis. Our model is developed using two datasets which jointly comprise over two million entries from Brazilian patients: The SaMi-Trop study focusing on ChD patients, enriched with data from the CODE study from the general population. The model's performance is evaluated on two external datasets: the REDS-II, a study focused on ChD with 631 patients, and the ELSA-Brasil study, with 13,739 civil servant patients.<h4>Findings</h4>Evaluating our model, we obtain an AUC-ROC of 0.80 (CI 95% 0.79-0.82) for the validation set (samples from CODE and SaMi-Trop), and in external validation datasets: 0.68 (CI 95% 0.63-0.71) for REDS-II and 0.59 (CI 95% 0.56-0.63) for ELSA-Brasil. In the latter, we report a sensitivity of 0.52 (CI 95% 0.47-0.57) and 0.36 (CI 95% 0.30-0.42) and a specificity of 0.77 (CI 95% 0.72-0.81) and 0.76 (CI 95% 0.75-0.77), respectively. Additionally, when considering only patients with Chagas cardiomyopathy as positive, the model achieved an AUC-ROC of 0.82 (CI 95% 0.77-0.86) for REDS-II and 0.77 (CI 95% 0.68-0.85) for ELSA-Brasil.<h4>Interpretation</h4>The neural network detects chronic Chagas cardiomyopathy (CCC) from ECG-with weaker performance for early-stage cases. Future work should focus on curating large higher-quality datasets. The CODE dataset, our largest development dataset includes self-reported and therefore less reliable labels, limiting performance for non-CCC patients. Our findings can improve ChD detection and treatment, particularly in high-prevalence areas.

Item Description

1935-2727
1935-2735
10.1371/journal.pntd.0011118