AIMC Topic: Chagas Disease
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Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.
Acta tropica
Apr 26, 2025
Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated ident...
Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.
PloS one
Mar 24, 2025
Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American continent and causes 12,000 deaths every year. CD clinically manifests in two phases (acute and chron...
Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.
Medical and veterinary entomology
Dec 13, 2024
The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomin...
Machine learning for predicting Chagas disease infection in rural areas of Brazil.
PLoS neglected tropical diseases
Apr 16, 2024
INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening comp...
Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.
PLoS neglected tropical diseases
Jul 19, 2022
BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. I...
Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.
PLoS neglected tropical diseases
Apr 14, 2022
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the hi...
Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.
PLoS neglected tropical diseases
Dec 6, 2021
BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm sh...
Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 13, 2021
BACKGROUND: Chagas disease (CD) and ischemic stroke (IS) have a close, but poorly understood, association. There is paucity of evidence on the ideal secondary prophylaxis and etiological determination, with few cardioembolic patients being identified...
Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae).
PLoS neglected tropical diseases
Mar 8, 2021
Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links betwe...
Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.
PLoS neglected tropical diseases
Jun 26, 2015
BACKGROUND: Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.
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