We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us

Olympus

Manufactures optical and digital equipment for the healthcare and consumer electronics sectors, including endoscopy a... read more Featured Products: More products

Download Mobile App




Automated Malaria Diagnosis Enhanced by Deep Neural Networks

By LabMedica International staff writers
Posted on 14 Aug 2020
Plasmodium falciparum malaria remains one of the greatest global health burdens with over 228 million cases globally in 2018. More...
In that year there were approximately 405,000 deaths due to malaria worldwide, with the African region accounting for 93% of these deaths, mostly among children.

Although there are a range of techniques that have been developed for the diagnosis of malaria, conventional light microscopy on Giemsa‐stained thick and thin blood films remains the gold standard. Techniques such as polymerase chain reaction, flow cytometric assay and fluorescence‐dye based approaches lack a universally standardized methodology, present high costs, and require quality control improvement.

A team of scientists from University College London (London, UK) leveraged routine clinical‐microscopy labels from their quality‐controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. The DeepMCNN system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitaemia estimation in MP/μL. Malaria parasites were detected and counted using human‐expert operated microscopy following Giemsa staining of thick and thin blood films. The criterion for declaring a participant to be malaria parasite‐free was no detectable parasites in 100 high‐power (100×) fields in thick films.

The investigators captured images using an upright bright-field BX63 microscope (Olympus, Tokyo, Japan) fitted with a 100×/1.4 NA objective lens, a motorized x‐y sample positioning stage (Prior Scientific, Cambridge, UK) and a color camera to capture images of Giemsa‐stained, thick blood smears. These smears prepared in their clinics tested the use of deep learning‐based object detection methods to identify both P. falciparum parasites and white‐blood‐cell (WBC) nuclei in the digitized extended depth of field (EDoF) thick blood films images.

The team reported that the prospective validation of the DeepMCNN achieved sensitivity/specificity of 0.92/0.90 against expert‐level malaria diagnosis. The PPV/NPV performance was 0.92/0.90, which is clinically usable in their holoendemic settings in a densely populated metropolis.

The authors concluded that their open data and easily deployable DeepMCNN provide a clinically relevant platform, where other healthcare providers could harness their readily available patient level diagnostic labels, to tailor and further improve the accuracy of the DeepMCNN classifier for their clinical pathway settings. The study was published in the August 2020 issue of the American Journal of Hematology.

Related Links:

University College London
Olympus
Prior Scientific

New
Gold Member
Immunochromatographic Assay
CRYPTO Cassette
POC Helicobacter Pylori Test Kit
Hepy Urease Test
New
8-Channel Pipette
SAPPHIRE 20–300 µL
New
Laboratory Software
ArtelWare
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Hematology

view channel
Image: New research points to protecting blood during radiation therapy (Photo courtesy of 123RF)

Pioneering Model Measures Radiation Exposure in Blood for Precise Cancer Treatments

Scientists have long focused on protecting organs near tumors during radiotherapy, but blood — a vital, circulating tissue — has largely been excluded from dose calculations. Each blood cell passing through... Read more

Immunology

view channel
Image: The test could streamline clinical decision-making by identifying ideal candidates for immunotherapy upfront (Xiao, Y. et al. Cancer Biology & Medicine July 2025, 20250038)

Blood Test Predicts Immunotherapy Efficacy in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking targeted therapies, making immunotherapy a promising yet unpredictable option. Current biomarkers such as PD-L1 expression or tumor... Read more

Microbiology

view channel
Image: New diagnostics could predict a woman’s risk of a common sexually transmitted infection (Photo courtesy of 123RF)

New Markers Could Predict Risk of Severe Chlamydia Infection

Chlamydia trachomatis is a common sexually transmitted infection that can cause pelvic inflammatory disease, infertility, and other reproductive complications when it spreads to the upper genital tract.... Read more

Technology

view channel
Image: The sensor can help diagnose diabetes and prediabetes on-site in a few minutes using just a breath sample (Photo courtesy of Larry Cheng/Penn State)

Graphene-Based Sensor Uses Breath Sample to Identify Diabetes and Prediabetes in Minutes

About 37 million U.S. adults live with diabetes, and one in five is unaware of their condition. Diagnosing diabetes often requires blood draws or lab visits, which are costly and inconvenient.... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.