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

Glaucoma: Overview01:25

Glaucoma: Overview

1.7K
Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
1.7K
Diabetic Retinopathy01:27

Diabetic Retinopathy

66
DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
66

You might also read

Related Articles

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

Sort by
Same author

An improved tortuosity measurement method combining curvature-based, breadth-first search and Euclidean distance for retinal image analysis.

PeerJ·2026
Same author

Spike train analysis in rehabilitation movement classification using deep learning approach.

Scientific reports·2025
Same author

Machine Learning-Based Computer Vision for Depth Camera-Based Physiotherapy Movement Assessment: A Systematic Review.

Sensors (Basel, Switzerland)·2025
Same author

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring.

Diagnostics (Basel, Switzerland)·2023
Same author

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis.

PeerJ·2022
Same author

Towards a Connected Mobile Cataract Screening System: A Future Approach.

Journal of imaging·2022
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K

Non-Invasive Dry Eye Disease Detection Using Infrared Thermography Images: A Proof-of-Concept Study.

Laily Azyan Ramlan1, Wan Mimi Diyana Wan Zaki1, Marizuana Mat Daud2

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.

Diagnostics (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Smartphone infrared thermography shows promise for detecting Dry Eye Disease (DED). This non-invasive screening method accurately identifies DED by analyzing ocular surface temperature changes, aiding early diagnosis.

Keywords:
Dry Eye Diseaseinfrared thermographymachine learningstatistical analysis

More Related Videos

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds
10:07

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Published on: November 6, 2015

13.6K
Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction
05:00

Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction

Published on: April 1, 2019

15.1K

Related Experiment Videos

Last Updated: May 6, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds
10:07

Thermal Imaging to Study Stress Non-invasively in Unrestrained Birds

Published on: November 6, 2015

13.6K
Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction
05:00

Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction

Published on: April 1, 2019

15.1K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Dry Eye Disease (DED) severely impacts quality of life due to tear film instability and reduced tear production.
  • Current diagnostic methods for DED are often invasive, non-portable, and time-consuming, leading to delayed diagnosis and treatment.
  • Limited access to eye care professionals exacerbates the challenges in DED management.

Purpose of the Study:

  • To investigate the feasibility of smartphone-based infrared thermography (IRT) as a non-invasive and portable screening tool for DED.
  • To assess the correlation between ocular surface temperature changes measured by IRT and established DED indicators like Tear Film Break-up Time (TBUT) and Ocular Surface Disease Index (OSDI).
  • To evaluate the efficacy of machine learning models in classifying normal versus DED eyes using IRT data.

Main Methods:

  • Infrared thermography (IRT) images were acquired from 40 subjects (22 normal, 58 DED).
  • Ocular surface temperature changes were analyzed at the nasal cornea, central cornea, and temporal cornea.
  • Statistical analyses, including correlations and independent t-tests, were performed. Machine learning models (SVM, k-NN) were employed for classification.

Main Results:

  • DED eyes exhibited significantly faster ocular surface cooling rates compared to normal eyes (p < 0.001).
  • TBUT showed significant correlations with OSDI (negative) and cooling rates in all corneal regions (positive).
  • Machine learning models demonstrated high classification accuracy, with k-NN achieving 91.89% and SVM achieving 90.54%.

Conclusions:

  • Smartphone-based IRT shows significant potential as a non-invasive screening tool for Dry Eye Disease.
  • The high accuracy of machine learning models in classifying DED using IRT data supports its clinical utility.
  • Further validation with larger, diverse datasets is required to facilitate clinical application of this technology.