Comparing Deep Learning CNN method with Traditional MRI-based Hippocampal segmentation and volumetry for Early Alzheimer’s Disease Diagnosis Across Diverse Populations

Abstract The advent of artificial intelligence (AI) driven software has impacted numerous aspects of medicine, leading to automated algorithms that assist in performing feature extraction, making measurements on diagnostic imaging, and aiding in diagnosing disorders. AI-based convoluted neural networks (CNN) enable automated segmentation of the hippocampal volume seen on MRI diagnostic imaging, hence facilitating the diagnosis of Alzheimer’s disease (AD). Traditional voxel-based morphometry (VBM) used for measuring hippocampal volume can be time-laborious, thus CNN-based algorithms can minimize the time and reduce human errors. We utilized HippoDeep, an open-source CNN-based algorithm, to compare the hippocampal datasets from a Caucasian population with a dataset from a Southeast Asian AD and cognitively healthy control (HC) population. ROC analysis revealed superior diagnostic performance for HippoDeep, with AUCs of 0.918 (left hippocampus) and 0.882 (right hippocampus), compared to VBM’s 0.788 and 0.741, respectively. We determined cut-off thresholds for hippocampal volume to further improve the classification method. CNN-based method outperformed traditional semiautomated for segmentation accuracy (p < 0.001) with insignificant interpopulation differences. Moreover, CNN-derived hippocampal volumes exhibited stronger correlations with MMSE scores (r = 0.63 vs. r = 0.42). HippoDeep offers accurate, reproducible, and generalizable hippocampal segmentation, supporting its potential as a clinical tool for early AD diagnosis across diverse populations.