AIMC Topic: Malaria, Falciparum

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Tile-based microscopic image processing for malaria screening using a deep learning approach.

BMC medical imaging Mar 22, 2023
BACKGROUND: Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enor...
Malaria Microscopy Malaria, Falciparum Humans Deep Learning Image Processing, Computer-Assisted
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Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future.

Photodiagnosis and photodynamic therapy Nov 12, 2022
Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. Accor...
Malaria, Falciparum Photochemotherapy Plasmodium Humans Deep Learning Malaria Female Microscopy Animals
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study.

BMJ open Mar 31, 2022
OBJECTIVES: Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to i...
Malaria Humans Malaria, Falciparum Deep Learning Climate Change China
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Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nature biomedical engineering Jun 10, 2021
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...
Male Plasmodium falciparum Spermatozoa Neural Networks, Computer Female Malaria, Falciparum Image Interpretation, Computer-Assisted Datasets as Topic Supervised Machine Learning Microscopy Embryo, Mammalian Humans Histocytochemistry Time-Lapse Imaging
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Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Spatial and spatio-temporal epidemiology Jul 4, 2020
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regress...
Nonlinear Dynamics Incidence Humans Malaria Prevalence Malaria, Falciparum
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Expert-level automated malaria diagnosis on routine blood films with deep neural networks.

American journal of hematology Apr 30, 2020
Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban hea...
Humans Neural Networks, Computer Malaria Malaria, Falciparum
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Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.

Trials Feb 10, 2020
BACKGROUND: Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements...
Quinine Malaria, Falciparum Asia Artesunate Plasmodium falciparum Antimalarials Machine Learning Humans Algorithms Retrospective Studies Adult Treatment Outcome Randomized Controlled Trials as Topic
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Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

Malaria journal Oct 7, 2019
BACKGROUND: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy couple...
Humans Logistic Models Plasmodium falciparum Spectrophotometry, Infrared Supervised Machine Learning Dried Blood Spot Testing Malaria, Falciparum Tanzania
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Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions.

International journal of environmental research and public health Sep 18, 2019
The future implications of climate change on malaria transmission at the global level have already been reported, however such evidences are scarce and limited in India. Here our study aims to assess, identify and map the potential effects of climate...
Asia Humidity Malaria, Vivax Models, Statistical Malaria, Falciparum Climate Change Humans Geographic Information Systems Temperature Fuzzy Logic Animals
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Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

Scientific reports Aug 27, 2018
The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated w...
Child, Preschool Humans Child Neural Networks, Computer Malaria Plasmodium falciparum Phenotype Male Anemia Biomarkers Severity of Illness Index Malaria, Falciparum Female
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