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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.
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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.

Efe Precious Onakpojeruo1, Dilber Uzun Ozsahin1,2, Berna Uzun1

  • 1Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin 10, Turkey.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary

Denoising Diffusion Models (DDM) generate synthetic medical images to improve AI dementia diagnosis, overcoming data imbalance and privacy issues. This approach achieved 98% accuracy, outperforming existing methods for early detection.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning

Background:

  • Medical imaging datasets for AI-driven dementia diagnosis face challenges due to data imbalance and patient privacy concerns.
  • Traditional data augmentation techniques like Generative Adversarial Networks (GANs) have limitations in addressing these issues.
  • This study explores Denoising Diffusion Models (DDM) as an advanced solution for synthetic data generation in medical AI.

Purpose of the Study:

  • To address data imbalance and privacy concerns in AI-based dementia diagnosis using synthetic data.
  • To evaluate the efficacy of Denoising Diffusion Models (DDM) in generating clinically relevant synthetic medical images.
  • To develop and assess a novel deep learning framework for dementia classification utilizing DDM-generated data.

Main Methods:

  • Utilized the Kaggle Alzheimer's MRI dataset, comprising axial MRIs across four dementia severity categories.
  • Integrated Denoising Diffusion Models (DDM) with a novel Conditional Deep Convolutional Neural Network (C-DCNN) for dementia stage classification.
  • Generated 2,560 synthetic images per class, preprocessing them via skull removal, grayscale conversion, and resizing to 128x128 pixels. Radiologist validation ensured clinical relevance.

Main Results:

  • The proposed Conditional Deep Convolutional Neural Network (C-DCNN) model achieved a high accuracy of 98% in dementia classification.
  • The C-DCNN model significantly outperformed established deep learning architectures including ResNet50, VGG16, VGG19, and InceptionV3.
  • DDM-generated synthetic data proved effective in enhancing classification performance.

Conclusions:

  • Denoising Diffusion Models (DDM) effectively generate synthetic datasets that improve dementia classification accuracy in AI applications.
  • The developed framework establishes a new benchmark for AI in healthcare, offering a scalable and robust solution.
  • This approach supports early dementia diagnosis and facilitates improved treatment planning.