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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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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...
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

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

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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...
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Related Experiment Video

Updated: Jan 7, 2026

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

Sounak Bhowmik1, Talita Perciano2, Himanshu Thapliyal1

  • 1University of Tennessee, Knoxville, TN, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Quantum transfer learning significantly enhances early dementia detection using MRI scans. Hybrid quantum-classical AI models show improved accuracy and sensitivity over traditional methods for Alzheimer's Disease and Related Dementias (ADRD) screening.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Quantum computing offers potential for early detection of Alzheimer's Disease and Related Dementias (ADRD).
  • Quantum-enhanced machine learning can screen Alzheimer's using MRI data from healthy and affected individuals.
  • This study explores quantum transfer learning to improve classical deep learning models for dementia detection.

Purpose of the Study:

  • Demonstrate quantum transfer learning's potential to enhance classical deep learning models for dementia detection.
  • Improve early screening of Alzheimer's disease using brain imaging data.
  • Advance healthcare technology through quantum-enhanced AI.

Main Methods:

  • Utilized OASIS-2 dataset of MRI sagittal images (64 demented, 72 non-demented subjects).
  • Applied quantum transfer learning by replacing dense layers of a classical model with a dressed quantum circuit.
  • Trained and validated models using 4-fold cross-validation on `default.qubit` and IonQ Aria-1 simulators.

Main Results:

  • Quantum transfer learning models showed significant performance enhancement over the classical baseline.
  • Accuracy improved by 6-14%, and sensitivity by 4-17% compared to classical methods.
  • Reduced misclassification rates for demented patients, indicating fewer false negatives.

Conclusions:

  • Quantum machine learning assists in enhancing dementia detection from brain images.
  • Quantum computing shows practical utility in Alzheimer's Disease and Related Dementias (ADRD) research.
  • Hybrid quantum-classical approaches offer a promising avenue for medical diagnostics.