AI-ECG for Detecting Left Ventricular Systolic Dysfunction in Chagas Disease: Diagnostic and Prognostic Value.

Saved in:
Bibliographic Details
Title: AI-ECG for Detecting Left Ventricular Systolic Dysfunction in Chagas Disease: Diagnostic and Prognostic Value.
Authors: Cardoso CS; Telehealth Center and Research Group in Epidemiology and Evaluation of New Technologies in Health, Federal University of São João del-Rei, Divinópolis, Brazil., Mangold K; Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA., Padilha da Silva JL; Universidade Federal do Paraná, Brazil., Di Lorenzo Oliveira C; Federal University of São João del-Rei, Divinópolis, Curitiba, Paraná, Brazil., Ferreira AM; State University of Montes Claros, Montes Claros, Brazil., Oliveira da Silva LC; Hospital das Clínicas de São Paulo FMUSP, São Paulo, Brazil., Nunes MDCP; Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Vinhal WC; Telehealth Center and Research Group in Epidemiology and Evaluation of New Technologies in Health, Federal University of São João del-Rei, Divinópolis, Brazil., Gonçalves ACO; Telehealth Center and Research Group in Epidemiology and Evaluation of New Technologies in Health, Federal University of São João del-Rei, Divinópolis, Brazil., Gomes PR; Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Perel P; London School of Hygiene & Tropical Medicine, London, England., Attia IZ; Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA., Sabino EC; University of São Paulo, São Paulo, Brazil., Lopez-Jimenez F; Cardiovascular Medicine, Mayo Clinic, Rochester, Minessota, USA., Ribeiro ALP; Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. Electronic address: alpr1963br@gmail.br.
Source: JACC. Advances [JACC Adv] 2026 Jul; Vol. 5 (7), pp. 102878.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918419284106676 Publication Model: Print Cited Medium: Internet ISSN: 2772-963X (Electronic) Linking ISSN: 2772963X NLM ISO Abbreviation: JACC Adv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2772-963X
DOI:10.1016/j.jacadv.2026.102878