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Related Concept Videos

Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...

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Related Experiment Video

Updated: May 28, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Development of a serverless, interactive application for Alzheimer's disease detection and visualization using MRI

Tri Huynh1,2, My Nguyen3, Huong T T Ha1,2

  • 1School of Biomedical Engineering, International University, Ho Chi Minh City, Vietnam.

The Neuroradiology Journal
|May 27, 2026
PubMed
Summary

This study developed an AI system for early Alzheimer's disease detection using MRI scans. The cost-effective, cloud-based tool offers high accuracy, aiding diagnosis in resource-limited settings.

Keywords:
Alzheimer’s diseaseFMRIB software libraryKolmogorov–Arnold networksMRI images processingneural architecture searchserverless computinguniversal inverted bottleneck

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Area of Science:

  • Neuroimaging and Artificial Intelligence
  • Computational Neuroscience
  • Medical Diagnostics

Background:

  • Early Alzheimer's disease (AD) detection is crucial for effective intervention with disease-modifying treatments.
  • Clinical implementation faces barriers like limited computational resources and the research-to-practice gap.
  • Resource-constrained healthcare facilities require accessible diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CADx) system for classifying cognitive states (Alzheimer's disease, Mild Cognitive Impairment, Cognitively Normal) from structural MRI.
  • To integrate automated preprocessing and cost-effective cloud deployment for resource-limited settings.
  • To assess the system's accuracy, specificity, and operational costs.

Main Methods:

  • A multi-view MRI analysis model was optimized using neural architecture search (NAS) with Universal Inverted Bottleneck blocks and Kolmogorov-Arnold Networks.
  • Automated MRI preprocessing via FSL was deployed on serverless cloud functions for scalable processing.
  • The system was evaluated on the ADNI dataset (1687 individuals) and a web application was developed for patient management and prediction.

Main Results:

  • The model achieved 86.7% accuracy and 0.900 AUC for three-class classification (AD, MCI, CN).
  • High specificity was observed across all classes (CN: 91.0%, MCI: 91.8%, AD: 97.3%), with 100% specificity for distinguishing AD from CN.
  • Operational costs were approximately $0.028 USD per diagnosis, demonstrating cost-effectiveness.

Conclusions:

  • The developed system offers a cost-effective solution for early Alzheimer's diagnosis, accessible to resource-constrained environments.
  • The integration of NAS and serverless deployment advances automated AD detection for clinical use.
  • Future research should focus on prospective clinical validation and incorporating interpretability features.