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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...

You might also read

Related Articles

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

Sort by

Same author

A Graph Neural Network-Based Multispectral-View Learning Model for Diabetic Macular Ischemia Detection From Color Fundus Photographs.

Translational vision science & technology·2026

Same author

Indices of Arterial Stiffness and the Alzheimer's Disease Biomarker, p-tau217, in Non-Demented Midlife Women.

Pulse (Basel, Switzerland)·2026

Same author

An AI-Based OCT System to Detect Diabetic Macular Edema: A Prospective Validation and Noninferiority Randomized Clinical Trial.

JAMA·2026

Same author

Artificial intelligence-based retinal imaging for brain health assessment: a scoping review.

The Lancet. Digital health·2026

Same author

Optical coherence tomography artefact burden predicts cognitive decline and incident dementia.

Frontiers in aging neuroscience·2026

Same author

AI-Driven Oculomics-The Brain-Retina Connection.

JAMA ophthalmology·2026

Same journal

Evidence for progressive neurodegeneration in iatrogenic cerebral amyloid angiopathy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same journal

Human brain connectome profiles mediate the relationship between pathology burden and clinical phenotypes in Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same journal

Kat5 cKO mouse replicates biological domain signatures associated with Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same journal

Evaluation of CSF and plasma tau species as fluid surrogate candidates for tau PET in prodromal to moderate Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same journal

Associations of self-reported obstructive sleep apnea with cognition and dementia risk in cognitively unimpaired middle-aged adults.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same journal

Inflammation profiles in Alzheimer's disease relate to cognition and neurodegeneration.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

Biomarkers.

Anran Ran^1,2, Herbert Y H Hui³, Xiaoyan Hu¹

¹Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, NA, Hong Kong.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association

|December 25, 2025

Summary

This summary is machine-generated.

Deep learning models using retinal optical coherence tomography (OCT) can accurately detect Alzheimer's Disease (AD). The Ensemble Model achieved 90.5% accuracy, showing potential for early AD identification and intervention.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

Related Experiment Videos

Last Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

Area of Science:

Ophthalmology
Neurology
Artificial Intelligence

Background:

Alzheimer's dementia (AD) is a growing public health concern, with nearly half of cases potentially preventable or delayable by addressing modifiable risk factors.
Early identification of AD is crucial for timely intervention and management.

Purpose of the Study:

To develop novel deep-learning (DL) models utilizing retinal optical coherence tomography (OCT) for automated Alzheimer's Disease (AD) detection.
To explore the efficacy of fusion network and ensemble learning techniques in identifying AD-dementia from OCT data.

Main Methods:

Trained and validated DL models using en face images and analysis reports (retinal nerve fibre layer, macular thickness, ganglion cell-inner plexiform layer) from Cirrus HD-OCT.
Developed fusion network models (ONH, Macula, Integrated) and an Ensemble Model integrating multiple OCT inputs for AD-dementia classification.

Main Results:

The Ensemble Model achieved the highest accuracies: 90.5% (internal validation), 80.3% (External-1), and 74.2% (External-2).
Other models (ONH, Macula, Integrated) also demonstrated significant diagnostic capabilities, with accuracies ranging from 70.7% to 85.4% across datasets.

Conclusions:

The proposed Ensemble Model effectively identifies AD-dementia by learning AD-related retinal features from OCT analysis.
This approach holds significant potential for accurate and early detection of Alzheimer's Disease.