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

You might also read

Related Articles

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

Sort by
Same author

Development of Attention-based Prediction Models for All-cause Mortality, Home Care Need, and Nursing Home Admission in Ageing Adults in Spain Using Longitudinal Electronic Health Record Data.

Journal of medical systems·2025
Same author

EMVC-2: an efficient single-nucleotide variant caller based on expectation maximization.

Bioinformatics (Oxford, England)·2023
Same author

Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review.

Journal of the American Medical Informatics Association : JAMIA·2023
Same author

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People.

JMIR public health and surveillance·2023
Same author

Dynamics of multimorbidity and frailty, and their contribution to mortality, nursing home and home care need: A primary care cohort of 1 456 052 ageing people.

EClinicalMedicine·2022
Same author

Random-Walk Laplacian for Frequency Analysis in Periodic Graphs.

Sensors (Basel, Switzerland)·2021

Related Experiment Video

Updated: Feb 20, 2026

Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages
08:23

Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages

Published on: May 30, 2019

12.2K

Counting malaria parasites with a two-stage EM based algorithm using crowsourced data.

Margarita Cabrera-Bean, Alba Pages-Zamora, Carles Diaz-Vilor

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    This study introduces a crowdsourced, human-machine approach for rapid malaria diagnosis. It uses Expectation-Maximization to accurately count malaria parasites in blood smear images, improving diagnostic reliability.

    More Related Videos

    Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
    10:50

    Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

    Published on: November 2, 2018

    8.5K
    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

    2.7K

    Related Experiment Videos

    Last Updated: Feb 20, 2026

    Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages
    08:23

    Phenotypic Analysis of Rodent Malaria Parasite Asexual and Sexual Blood Stages and Mosquito Stages

    Published on: May 30, 2019

    12.2K
    Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
    10:50

    Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

    Published on: November 2, 2018

    8.5K
    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

    2.7K

    Area of Science:

    • Medical diagnostics
    • Computational biology
    • Parasitology

    Background:

    • Malaria eradication is a key global health objective for the World Health Organization (WHO).
    • Accurate and rapid malaria diagnosis is crucial for effective treatment and control efforts.
    • Existing diagnostic methods can be costly, slow, or lack reliability in challenging conditions.

    Purpose of the Study:

    • To develop a low-cost, fast, and reliable malaria diagnostic method using human-machine interaction.
    • To address the technical challenge of detecting malaria parasite spots in images amidst artifacts.
    • To leverage a crowdsourced approach for improved diagnostic accuracy.

    Main Methods:

    • Utilized human-machine interaction strategies within a crowdsourced framework.
    • Modeled annotator clicks/tags as a robust finite mixture.
    • Applied Expectation-Maximization (EM) algorithm for parasite counting on Giemsa-stained thick blood smear images.

    Main Results:

    • The proposed Expectation-Maximization (EM) algorithm accurately counts malaria parasites in microscopic images.
    • The crowdsourced human-machine approach demonstrated superior performance compared to traditional methods.
    • Effective detection of malaria parasite spots was achieved even under harsh imaging conditions with similar artifacts.

    Conclusions:

    • The developed human-machine interaction strategy offers a promising solution for low-cost, rapid malaria diagnosis.
    • Crowdsourced data combined with EM algorithm provides a robust method for parasite quantification.
    • This approach has the potential to significantly contribute to global malaria eradication efforts.