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Related Experiment Video

Updated: Mar 29, 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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Alzheimer's Disease Classification Using Population-Referenced Brain Volumetric Percentiles.

Jae Hyuk Shim1, Hyeon-Man Baek1,2

  • 1MTech Lab Co., Ltd., Room B1027, 119, Songdo Munhwa-ro, Yeonsu-gu, Incheon 21985, Republic of Korea.

Brain Sciences
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Population-referenced brain volumetric percentiles accurately distinguish Alzheimer's disease (AD) from cognitively normal individuals. This novel approach shows robust generalization across diverse cohorts and scanner types, offering a promising diagnostic tool.

Keywords:
Alzheimer’s diseaseMRIneuroimagingsegmentationstructural analysisvolumetric analysis

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Area of Science:

  • Neuroimaging
  • Biomarkers
  • Disease Classification

Background:

  • Interpreting raw brain volumes for individual Alzheimer's disease (AD) diagnosis is difficult without longitudinal data or matched controls.
  • Automated brain volumetric analysis faces challenges in clinical translation due to variability in interpretation.

Purpose of the Study:

  • To develop and validate a classification model using population-referenced volumetric percentiles for distinguishing AD from cognitively normal (CN) subjects.
  • To assess the generalization capability of the model across independent cohorts and varying scanner protocols.

Main Methods:

  • Extracted brain volumes from 95 regions using automated segmentation.
  • Converted volumes to age and sex-adjusted percentiles using a reference population (N=1833).
  • Trained a logistic regression classifier on ADNI data and evaluated on internal and external validation sets (including a Korean cohort).

Main Results:

  • The classification model demonstrated excellent discrimination across all evaluation sets, with AUCs ranging from 0.960 to 0.981 and accuracies from 87.5% to 90.3%.
  • Minimal validation gap (0.018) confirmed robust generalization across different populations and scanner protocols.
  • Analysis revealed AD-associated atrophy patterns (ventricular regions) and protective patterns (medial temporal structures).

Conclusions:

  • Population-referenced brain volumetric percentiles provide an accurate and generalizable method for AD classification.
  • This approach contextualizes individual brain structure against normative data, accounting for age and sex.
  • The method shows potential as an accessible neuroimaging-based diagnostic tool for clinical use.