Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Inflammatory bowel disease outcomes among hospitalized homeless individuals.

BMC gastroenterology·2026

Same author

Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

To Evaluate the Efficacy of Zero-Shot Prompting Using Large Language Models in the Extraction of Microbial Keratitis Descriptors.

Cornea·2025

Same author

Optimizing Ovarian Stimulation for IVF in PCOS Patients: A Novel Day 1 GnRH Antagonist Protocol.

Journal of clinical medicine·2025

Same author

Ensemble learning to enhance accurate identification of patients with glaucoma using electronic health records.

JAMIA open·2025

Same author

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

Ophthalmology science·2025

Same journal

Exploring the Potential of Ambient AI for Inpatient Documentation: A Qualitative Study with Junior Doctors.

Journal of medical systems·2026

Same journal

Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.

Journal of medical systems·2026

Same journal

Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation.

Journal of medical systems·2026

Same journal

Correction to: Designing Operating Rooms as an Integrated Socio-Technical Ecosystem: Practical Lessons from a High-Volume Tertiary Center.

Journal of medical systems·2026

Same journal

AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.

Journal of medical systems·2026

Same journal

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Journal of medical systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

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

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

Published on: October 27, 2023

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

Maitreya Maity¹, Dhiraj Dhane¹, Tushar Mungle¹

¹School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.

Journal of Medical Systems

|October 28, 2017

Summary

This summary is machine-generated.

A web-based system uses machine learning to analyze malaria parasite images from microscopic blood smears. This automated approach achieves high accuracy, improving remote healthcare diagnostics in underserved areas.

Keywords:

Computer-aided diagnosis Electronic healthcare system Feature extraction Feature selection Malaria screening Supervised classification

More Related Videos

A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

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

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

Related Experiment Videos

Last Updated: Feb 20, 2026

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

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

Published on: October 27, 2023

A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

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

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

Area of Science:

* Medical Informatics
* Computer Science
* Parasitology

Background:

* Conventional healthcare delivery can be enhanced by web-enabled e-healthcare systems and computer-assisted disease diagnosis.
* Distributed healthcare networks improve quality and service by connecting peripheral centers to a central server for remote assistance.

Purpose of the Study:

* To design and develop a web-based distributed healthcare management system for malaria diagnosis.
* To quantitatively evaluate microscopic images of malaria parasites using a machine learning approach.
* To assess the feasibility of the system in rural areas with limited healthcare facilities.

Main Methods:

* Automated evaluation of malaria parasites involves pre-processing blood smear images and segmenting erythrocytes.
* Extraction of 138 quantitative features (color, morphology, texture) from segmented erythrocytes.
* Application of four feature selection methods (CFS, Chi-square, Information Gain, RELIEF) with three classifiers (Naive Bayes, C4.5, IB1) within a pattern classification framework.

Main Results:

* The proposed method achieved high diagnostic precision, with 99.2% sensitivity and 99.6% specificity when combining Correlation-based Feature Selection (CFS) and C4.5.
* This performance surpassed other tested feature selection and classification combinations.
* The web-based tool was developed using open standards (Java, ImageJ, WEKA) for broad accessibility.

Conclusions:

* The developed web-based system effectively automates malaria parasite detection from microscopic images.
* The system demonstrates high accuracy and specificity, offering a valuable tool for remote and rural healthcare.
* Integration of machine learning and distributed computing enhances diagnostic capabilities in resource-limited settings.