AIMC Topic: Leishmaniasis, Cutaneous

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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

PLoS neglected tropical diseases Mar 11, 2025
BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progres...
Humans Machine Learning Young Adult Adult Male Biomarkers Female Sudan Leishmaniasis, Visceral Leishmania donovani Adolescent Child Leishmaniasis, Cutaneous Middle Aged Disease Progression
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Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data.

BMC medical informatics and decision making Mar 10, 2025
BACKGROUND: As part of qualitative research, the thematic analysis is time-consuming and technical. The rise of generative artificial intelligence (A.I.), especially large language models, has brought hope in enhancing and partly automating thematic ...
Male Qualitative Research Leishmaniasis, Cutaneous Female Adult Humans Artificial Intelligence
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Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions.

Transboundary and emerging diseases Jan 21, 2025
Cutaneous leishmaniasis (CL) remains a significant global public health disease, with the critical distinction and exact detection between responsive and unresponsive cases dictating treatment strategies and patient outcomes. However, image-based met...
Leishmaniasis, Cutaneous Humans Deep Learning
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Single-nucleotide polymorphisms in genes associated with the vitamin D pathway related to clinical and therapeutic outcomes of American tegumentary leishmaniasis.

Frontiers in cellular and infection microbiology Jan 8, 2025
BACKGROUND: The vitamin D pathway contributes to the microbicidal activity of macrophages against infection. In addition to induction of this pathway, interferon-gamma (IFNγ), interleukin (IL)-15, and IL32γ are part of a network of pro-inflammatory ...
Leishmaniasis, Cutaneous Male Female Polymorphism, Single Nucleotide 25-Hydroxyvitamin D3 1-alpha-Hydroxylase Treatment Outcome Middle Aged Genotype Vitamin D Young Adult Humans Receptors, Calcitriol Genetic Predisposition to Disease Cytokines Adult
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Comparison of the spatial and temporal distribution of cutaneous and mucosal leishmaniasis in the state of Rio de Janeiro between 2001 and 2011.

PloS one Dec 31, 2024
OBJECTIVE: To compare the spatio-temporal distribution of cutaneous leishmaniasis (CL) cases with mucosal leishmaniasis (ML) cases in the state of Rio de Janeiro (RJ) between 2001 and 2011.
Leishmaniasis, Mucocutaneous Incidence Child Leishmaniasis, Cutaneous Humans Adult Male Spatio-Temporal Analysis Female Brazil Adolescent
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Sensors (Basel, Switzerland) Dec 21, 2024
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...
Image Processing, Computer-Assisted Support Vector Machine Microscopy Humans Leishmaniasis, Cutaneous Machine Learning Leishmania Algorithms
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Fuzzy and spatial analysis of cutaneous leishmaniasis in Pará State, Brazilian Amazon: an ecological and exploratory study.

Journal of infection in developing countries Jul 29, 2024
INTRODUCTION: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022.
Fuzzy Logic Spatial Analysis Humans Risk Factors Socioeconomic Factors Leishmaniasis, Cutaneous Brazil
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Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

Acta tropica Sep 7, 2018
The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution ...
Machine Learning Ecosystem Geographic Information Systems Animals Phlebotomus Iran Leishmaniasis, Cutaneous Environment Area Under Curve Insect Vectors
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A new diagnostic method and tool for cutaneous leishmaniasis based on artificial intelligence techniques.

Computers in biology and medicine Jun 1, 2025
BACKGROUND: Cutaneous leishmaniasis (CL) is a parasitic disease caused by protozoan parasites of the genus Leishmania, leading to significant morbidity in endemic regions. While effective, traditional diagnostic methods often suffer from limitations ...
Image Processing, Computer-Assisted Leishmaniasis, Cutaneous Artificial Intelligence Humans Leishmania Deep Learning
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