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

Glucose Homeostasis: Regulation of Blood Glucose01:02

Glucose Homeostasis: Regulation of Blood Glucose

Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
During fasting, when blood glucose levels are low, the pancreas secretes glucagon. it...

You might also read

Related Articles

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

Sort by
Same author

Development of colorimetric and machine learning based accurate glucose detection platform for point of care applications.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 10, 2026

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
12:02

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Published on: April 11, 2016

12.1K

An accurate glucose detection platform using colorimetry and supervised learning algorithms.

Mithun Kanchan1, Pragna Harish1, Omkar S Powar1

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.

Biomedical Physics & Engineering Express
|March 9, 2026
PubMed
Summary

This study presents an affordable, precise Point-of-Care diagnostic platform for rapid blood glucose monitoring using microfluidics and image analysis. The system achieves high accuracy, offering a scalable solution for diverse healthcare settings.

Keywords:
glucoseimage processingmicrofluidicspoint-of-caresupervised learning algorithms

More Related Videos

Highly Sensitive and Rapid Fluorescence Detection with a Portable FRET Analyzer
08:27

Highly Sensitive and Rapid Fluorescence Detection with a Portable FRET Analyzer

Published on: October 1, 2016

9.6K
Bergmeyer Glucose Quantification for Microbiological Samples
07:23

Bergmeyer Glucose Quantification for Microbiological Samples

Published on: January 17, 2025

1.4K

Related Experiment Videos

Last Updated: May 10, 2026

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
12:02

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Published on: April 11, 2016

12.1K
Highly Sensitive and Rapid Fluorescence Detection with a Portable FRET Analyzer
08:27

Highly Sensitive and Rapid Fluorescence Detection with a Portable FRET Analyzer

Published on: October 1, 2016

9.6K
Bergmeyer Glucose Quantification for Microbiological Samples
07:23

Bergmeyer Glucose Quantification for Microbiological Samples

Published on: January 17, 2025

1.4K

Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Medical Diagnostics

Background:

  • Accurate blood glucose monitoring is crucial for managing diabetes and preventing complications.
  • Existing methods can be costly, time-consuming, or require specialized equipment.
  • Point-of-Care (POC) diagnostics offer potential for rapid, decentralized glucose assessment.

Purpose of the Study:

  • To develop an affordable, reliable, and precise POC diagnostic platform for glucose detection.
  • To integrate microfluidic and colorimetric principles with image analysis for glucose estimation.
  • To create a user-friendly system suitable for real-time monitoring in various healthcare settings.

Main Methods:

  • Fabrication of a custom microfluidic chip for enzymatic color reactions with minimal sample volume (~20 µL).
  • Development of a compact, 3D-printed imaging module with a high-resolution camera for stable image acquisition.
  • Application of supervised machine learning models (Random Forest, SVM, KNN, FNN) on engineered image features for glucose level prediction.

Main Results:

  • The Random Forest model achieved 98% cross-validation precision and near 100% specificity for glucose level distinction.
  • The system demonstrated rapid color development within 3-4 minutes and minimal misclassification (mean AUC near 1).
  • The platform is USB-powered, compatible with embedded systems/laptops, and eliminates the need for smartphones or external calibration.

Conclusions:

  • The proposed image-based glucose estimation approach is a cost-effective, scalable, and accurate POC solution.
  • The system's low reagent consumption, rapid analysis, and ease of operation are advantageous for decentralized screening and resource-limited settings.
  • Future work includes expanding concentration ranges, clinical validation, and automated calibration for enhanced usability.