AI Medical Compendium Topic

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Malaria

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Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic.

PloS one
BACKGROUND: Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cas...

Specialist hybrid models with asymmetric training for malaria prevalence prediction.

Frontiers in public health
Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical c...

An automated malaria cells detection from thin blood smear images using deep learning.

Tropical biomedicine
Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effect...

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...

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...

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...

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

The Lancet. Planetary health
BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogene...

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...

MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever.

Journal of travel medicine
BACKGROUND: Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence...