Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

77
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
77

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A large multi-focus dataset for white blood cell classification.

Scientific data·2024
Same author

Enhancing malaria detection in resource-limited areas: A high-performance colorimetric LAMP assay for Plasmodium falciparum screening.

PloS one·2024
Same author

Hybrid Bladder Phantom to Validate Next-Generation Optical Wearables for Neurogenic Bladder Volume Monitoring.

International neurourology journal·2023
Same author

Quantification of solution-free red blood cell staining by sorption kinetics of Romanowsky stains to agarose gels.

Analytical methods : advancing methods and applications·2023
Same author

Advancing Patient Care: Innovative Use of Near-Infrared Spectroscopy for Monitoring Urine Volume in Neurogenic Bladder.

International neurourology journal·2023
Same author

Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings.

Frontiers in artificial intelligence·2022

Related Experiment Video

Updated: May 5, 2026

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

14.2K

Embedded-deep-learning-based sample-to-answer device for on-site malaria diagnosis.

Chae Yun Bae1, Young Min Shin1, Mijin Kim1

  • 1Noul Co., Ltd., Yongin-si, Republic of Korea.

Frontiers in Bioengineering and Biotechnology
|August 5, 2024
PubMed
Summary
This summary is machine-generated.

A new miLab™ device uses solid hydrogel staining and deep learning for accurate malaria diagnosis. This digital microscopy tool achieves high accuracy in classifying infected red blood cells and shows promise for decentralized on-site testing.

Keywords:
automated staining processdeep-learning algorithmsdigital microscopymalaria diagnosismicroscopy examination

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.2K

Related Experiment Videos

Last Updated: May 5, 2026

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

14.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.2K

Area of Science:

  • Medical Diagnostics
  • Parasitology
  • Biomedical Engineering

Background:

  • Accurate malaria diagnosis at the cellular level is crucial for effective treatment and control.
  • Digital microscopy offers potential for improved malaria detection but requires enhanced algorithms and consistent sample preparation.
  • Current methods often involve complex equipment and liquid reagents, hindering on-site applicability.

Purpose of the Study:

  • To develop and evaluate a novel digital microscopy device (miLab™) for consistent and accurate malaria parasite detection.
  • To integrate a solid hydrogel staining method with deep learning for automated blood film preparation and analysis.
  • To assess the clinical performance of the miLab™ system for on-site malaria diagnosis.

Main Methods:

  • A novel miLab™ device utilizing a solid hydrogel staining method for consistent blood film preparation was developed.
  • Deformable staining patches were employed to ensure reproducible, high-quality blood films across varying hematocrits.
  • An embedded deep learning algorithm analyzed autofocused images from the miLab™ device for malarial parasite detection and classification.

Main Results:

  • The miLab™ system demonstrated consistent, high-quality, and reproducible blood films.
  • The deep learning algorithm achieved 98.86% accuracy in classifying infected red blood cells (RBCs).
  • Clinical validation in Malawi showed an overall percent agreement of 92.21% compared to manual microscopy.

Conclusions:

  • The miLab™ device offers a reliable and efficient tool for decentralized malaria diagnosis.
  • This novel approach minimizes human error and enables remote expert review through digital image transmission.
  • The miLab™ system has the potential to set a new paradigm for on-site malaria detection and diagnosis.