AIMC Topic: Leishmania
Clear Filters Showing 1 to 10 of 11 articles
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...
Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.
Molecular diversity
Dec 10, 2024
Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current...
Approved drugs successfully repurposed against based on machine learning predictions.
Frontiers in cellular and infection microbiology
Sep 26, 2024
Drug repurposing is a promising approach towards the discovery of novel treatments against Neglected Tropical Diseases, such as Leishmaniases, presenting the advantage of reducing both costs and duration of the drug discovery process. In previous wor...
Using machine learning to dissect host kinases required for Leishmania internalization and development.
Molecular and biochemical parasitology
Aug 22, 2024
The Leishmania life cycle alternates between promastigotes, found in the sandfly, and amastigotes, found in mammals. When an infected sandfly bites a host, promastigotes are engulfed by phagocytes (i.e., neutrophils, dendritic cells, and macrophages)...
DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.
BMC medical imaging
Jun 18, 2024
BACKGROUND: Leishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of...
A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine.
BMC infectious diseases
Jun 1, 2024
BACKGROUND: Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania par...
Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.
Transboundary and emerging diseases
Jan 17, 2024
, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subj...
Antileishmanial activity of novel indolyl-coumarin hybrids: Design, synthesis, biological evaluation, molecular docking study and in silico ADME prediction.
Bioorganic & medicinal chemistry letters
Dec 24, 2015
In present work we have designed and synthesized total twelve novel 3-(3-(1H-indol-3-yl)-3-phenylpropanoyl)-4-hydroxy-2H-chromen-2-one derivatives 13(a-l) using Ho(3+) doped CoFe2O4 nanoparticles as catalyst and evaluated for their potential antileis...
Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.
Computational biology and chemistry
Aug 1, 2025
Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have bee...
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 ...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 32006
- Neural Networks, Computer 30221
- Algorithms 27397
- Deep Learning 26901
- Artificial Intelligence 24835
- Robotics 13387
- Image Processing, Computer-Assisted 9735
- Retrospective Studies 9342
- Magnetic Resonance Imaging 6346
- Reproducibility of Results 5670
- Tomography, X-Ray Computed 4865
- Support Vector Machine 4769
- Computational Biology 4332
- Brain 4129
- Computer Simulation 3935
Recent Journals
- Scientific reports 6775
- Sensors (Basel, Switzerland) 4794
- PloS one 4200
- Neural networks : the official journal of the International Neural Network Society 3026
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1916
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1378
- IEEE journal of biomedical and health informatics 1172
- Journal of chemical information and modeling 1064
- Nature communications 1044
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 873
- Journal of medical Internet research 861
- Briefings in bioinformatics 853