AI Medical Compendium Journal:
PLoS neglected tropical diseases

Showing 11 to 20 of 31 articles

Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches.

PLoS neglected tropical diseases
Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climat...

Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.

PLoS neglected tropical diseases
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and req...

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

PLoS neglected tropical diseases
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...

Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.

PLoS neglected tropical diseases
INTRODUCTION: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microsco...

Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: A pilot study.

PLoS neglected tropical diseases
BACKGROUND: Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic suppo...

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.

PLoS neglected tropical diseases
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...

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.

PLoS neglected tropical diseases
BACKGROUND: Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements ...

Deep learning models for forecasting dengue fever based on climate data in Vietnam.

PLoS neglected tropical diseases
BACKGROUND: Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to clima...

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

PLoS neglected tropical diseases
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
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...