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

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Jidling, Carl, Gedon, Daniel, Schön, Thomas B., Oliveira, Claudia Di Lorenzo, Cardoso, Clareci Silva C., Ferreira, Ariela Mota, Giatti, Luana, Barreto, Sandhi Maria, Sabino, Ester, Ribeiro, Antonio L. P., Horta Ribeiro, Antônio
• Format: Article in Journal/Newspaper
• Language: English
• Published: Uppsala universitet, Institutionen för informationsteknologi 2023
• Subjects: Cardiac and Cardiovascular Systems; Kardiologi; sami
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-508548; https://doi.org/10.1371/journal.pntd.0011118

Chagas disease (ChD) is a neglected tropical disease, and the diagnosis relies on blood testing of patients from endemic areas. However, there is no clear recommendation on how to select patients for testing in endemic regions. Since most cases of Chronic ChD are asymptomatic, the diagnostic rates are low, preventing patients from receiving adequate treatment. The Electrocardiogram (ECG) is a widely available, low-cost exam, often available in primary care settings. We present an Artificial intelligence (AI) model for automatically detecting ChD from the ECG. AI algorithms have allowed the detection of hidden conditions on the ECG and, to the best of our knowledge, this is the first study that does it for ChD. We utilize large cohorts of patients from the relevant population of all-comers in affected regions in Brazil to develop a model for ChD detection that is then validated on datasets with ground truth labels obtained directly from the patients’ serological status. Our findings demonstrate a promising AI-ECG-based model for discriminating patients with chronic Chagas cardiomyopathy (CCC). The capacity of detecting ChD patients without CCC is still limited. But we believe this can be improved with the addition of epidemiological questions, and that such models can become useful tools for pre-selecting patients for further testing. 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 ...