AI Medical Compendium Topic

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Parasites

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Ontological representation, modeling, and analysis of parasite vaccines.

Journal of biomedical semantics
BACKGROUND: Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vacci...

Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.

Scientific reports
Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by pro...

Comprehensive stiffness regulation on multi-section snake robot with considering the parasite motion and friction effects.

Bioinspiration & biomimetics
Snake robots have been widely used in challenging environments, such as confined spaces. However, most existing snake robots with large length/diameter ratios have low stiffness, and this limits their accuracy and utility. To remedy this, a novel 'ma...

FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.

Medical image analysis
Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this pr...

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model.

Journal of visualized experiments : JoVE
Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease tran...

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.

Genome biology
Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSE...

Parasitic egg recognition using convolution and attention network.

Scientific reports
Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Ov...

A knowledge-integrated deep learning framework for cellular image analysis in parasite microbiology.

STAR protocols
Cellular image analysis is an important method for microbiologists to identify and study microbes. Here, we present a knowledge-integrated deep learning framework for cellular image analysis, using three tasks as examples: classification, detection, ...

A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification.

PloS one
Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel d...

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Sensors (Basel, Switzerland)
Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even de...