{"doi":"10.1371/journal.pntd.0011118","title":"Screening for Chagas disease from the electrocardiogram using a deep neural network","abstract":"BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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.","journal":"PLoS neglected tropical diseases","year":2023,"id":348040,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9643,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1089925,"name":"Daniel Gedon","orcid":"0000-0003-4397-9952","position":1,"is_corresponding":false},{"id":621707,"name":"Thomas B. Schön","orcid":"0000-0001-5183-234X","position":2,"is_corresponding":false},{"id":339102,"name":"Cláudia Di Lorenzo Oliveira","orcid":"0000-0001-8533-8155","position":3,"is_corresponding":false},{"id":339106,"name":"Clareci Silva Cardoso","orcid":"0000-0003-0689-1644","position":4,"is_corresponding":false},{"id":328871,"name":"Ariela Mota Ferreira","orcid":"0000-0002-2315-5318","position":5,"is_corresponding":false},{"id":621704,"name":"Luana Giatti","orcid":"0000-0001-5454-2460","position":6,"is_corresponding":false},{"id":621705,"name":"Sandhi Maria Barreto","orcid":"0000-0001-7383-7811","position":7,"is_corresponding":false},{"id":149534,"name":"Ester Sabino","orcid":"0000-0003-2623-5126","position":8,"is_corresponding":false},{"id":61511,"name":"Antônio Luiz Pinho Ribeiro","orcid":"0000-0002-2740-0042","position":9,"is_corresponding":false},{"id":621700,"name":"Antônio H. Ribeiro","orcid":"0000-0003-3632-8529","position":10,"is_corresponding":false},{"id":1089924,"name":"Carl Jidling","orcid":"0000-0002-6028-8961","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T01:12:01.859977Z","pmid":"37399207","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}