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
Sign In
Advertise with Us
PURITAN MEDICAL

Download Mobile App




Automated Microscopy Compared for Routine Malaria Diagnosis

By LabMedica International staff writers
Posted on 10 Oct 2018
Print article
Image: The Autoscope uses deep-learning software to quantify the malaria parasites in a sample (Photo courtesy of Intellectual Ventures).
Image: The Autoscope uses deep-learning software to quantify the malaria parasites in a sample (Photo courtesy of Intellectual Ventures).
Microscopic examination of Giemsa-stained blood films remains a major form of diagnosis in malaria case management. However, as with other visualization-based diagnoses, accuracy depends on individual technician performance, making standardization difficult and reliability poor.

Automated image recognition based on machine-learning, utilizing convolutional neural networks, offers potential to overcome these drawbacks. The application of digital image recognition to malaria microscopy, using artificial intelligence algorithms to replace or supplement the human factor in blood film interpretation, have been attempted, usually on thin films.

A team of scientists collaborating with Intellectual Ventures (Bellevue, WA, USA) conducted a cross-sectional, observational trial was conducted at two peripheral primary health facilities in Peru. They enrolled 700 participants whose age was between 5 and 75 years, and had a history of fever in the last three days or elevated temperature on admission. A finger prick blood sample was taken to create blood films for microscopy diagnosis, and additional drops of blood were spotted onto filter paper for subsequent quantitative polymerase chain reaction (qPCR) analysis. A prototype digital microscope device employing an algorithm based on machine-learning, the Autoscope, was assessed for its potential in malaria microscopy.

The investigators reported that at one clinic, sensitivity of Autoscope for diagnosing malaria was 72% and specificity was 85%. Microscopy performance was similar to Autoscope, with sensitivity 68% and specificity 100%. At one clinic, 85% of prepared slides had a minimum of 600 white blood cells (WBCs) imaged, thus meeting Autoscope’s design assumptions. At the second clinic, the sensitivity of Autoscope was 52% and specificity was 70%. Microscopy performance at this second clinic was 42% and specificity was 97%. Only 39% of slides from this clinic met Autoscope’s design assumptions regarding WBCs imaged.

The authors concluded that Autoscope’s diagnostic performance was on par with routine microscopy when slides had adequate blood volume to meet its design assumptions, as represented by results from one clinic. Autoscope’s diagnostic performance was poorer than routine microscopy on slides from the other clinic, which generated slides with lower blood volumes. The study was published on September 25, 2018, in the Malaria Journal.

Related Links:
Intellectual Ventures

Platinum Member
COVID-19 Rapid Test
OSOM COVID-19 Antigen Rapid Test
Specimen Collection & Transport
POCT Fluorescent Immunoassay Analyzer
FIA Go
New
Gold Member
Fully Automated Cell Density/Viability Analyzer
BioProfile FAST CDV

Print article

Channels

Clinical Chemistry

view channel
Image: Reaching speeds up to 6,000 RPM, this centrifuge forms the basis for a new type of inexpensive, POC biomedical test (Photo courtesy of Duke University)

POC Biomedical Test Spins Water Droplet Using Sound Waves for Cancer Detection

Exosomes, tiny cellular bioparticles carrying a specific set of proteins, lipids, and genetic materials, play a crucial role in cell communication and hold promise for non-invasive diagnostics.... Read more

Molecular Diagnostics

view channel
Image: The study showed the blood-based cancer screening test detects 83% of people with colorectal cancer with specificity of 90% (Photo courtesy of Guardant Health)

Blood Test Shows 83% Accuracy for Detecting Colorectal Cancer

Colorectal cancer is the second biggest cause of cancer deaths among adults in the U.S., with forecasts suggesting 53,010 people might die from it in 2024. While fewer older adults are dying from this... Read more

Hematology

view channel
Image: The Gazelle Hb Variant Test (Photo courtesy of Hemex Health)

First Affordable and Rapid Test for Beta Thalassemia Demonstrates 99% Diagnostic Accuracy

Hemoglobin disorders rank as some of the most prevalent monogenic diseases globally. Among various hemoglobin disorders, beta thalassemia, a hereditary blood disorder, affects about 1.5% of the world's... Read more

Industry

view channel
Image: These new assays are being developed for use on the recently introduced DxI 9000 Immunoassay Analyzer (Photo courtesy of Beckman Coulter)

Beckman Coulter and Fujirebio Expand Partnership on Neurodegenerative Disease Diagnostics

Beckman Coulter Diagnostics (Brea, CA, USA) and Fujirebio Diagnostics (Tokyo, Japan) have expanded their partnership focused on the development, manufacturing and clinical adoption of neurodegenerative... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.