AI Medical Compendium Journal:
Acta tropica

Showing 1 to 10 of 15 articles

Lidocaine exhibits antileishmanial and immunomodulatory activities in an in vitro model of tegumentary leishmaniasis.

Acta tropica
Local anesthetics have been shown to inhibit or delay the progression of tegumentary leishmaniasis (TL) in hamster models, although the mechanisms underlying this clinical improvement remain unclear. This study aimed to determine whether lidocaine re...

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Acta tropica
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...

Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Acta tropica
Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapp...

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Acta tropica
Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existin...

An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model.

Acta tropica
Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

Acta tropica
The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people's immunity vary across several factors. After the pandemic, real-life post-vaccination anom...

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Acta tropica
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collaps...

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Acta tropica
Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts i...

A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

Acta tropica
OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a ...

Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.

Acta tropica
Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) em...