AIMC Topic: Malaria

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Pursuing the elusive footsteps of malaria in peripheral blood smears utilizing artificial intelligence.

British journal of haematology
For over a century, the need to identify malaria in the peripheral blood has been the driving force behind the development of fundamental clinical microscopy techniques. In the study by Moysis et al., artificial intelligence-based model was utilized ...

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia.

British journal of haematology
In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the...

Efficient deep learning-based approach for malaria detection using red blood cell smears.

Scientific reports
Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting,...

Predicting malaria outbreak in The Gambia using machine learning techniques.

PloS one
Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic growth of nations in the tropical and subtropical parts of the world. This study aims to address this...

Predicting the age of field mosquitoes using mass spectrometry and deep learning.

Science advances
Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of mosquitoes is key for epidemiological monitoring of malaria transmission and evaluation of vector control strategies targeting...

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis.

Neural networks : the official journal of the International Neural Network Society
Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper,...

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.

Parasites & vectors
BACKGROUND: Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technician...

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Journal of chemical information and modeling
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in o...

The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas.

International journal of molecular sciences
Even if malaria is rare in Europe, it is a medical emergency and programs for its control should ensure both an early diagnosis and a prompt treatment within 24-48 h from the onset of the symptoms. The increasing number of imported malaria cases as w...

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.

Scientific reports
Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Dee...