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Background <p>Brain volumetry software is widely accepted for assessment in Alzheimer’s disease. However, direct comparisons for software-specific prediction capabilities across cognitive domains have not been reported. This study evaluates the performance of four brain volumetry software programs in predicting domain-specific cognitive scores in patients with early Alzheimer’s disease (AD) using a consistent deep learning model.</p> Methods <p>A total of 255 patients with amyloid PET-confirmed AD were retrospectively enrolled. Brain volumetric features were extracted from 3D T1-weighted MRI using four software programs (AQUA, DeepBrain, A-finder, and FreeSurfer). A multi-layer perceptron model incorporating seven clinical variables and software-specific volumetric features was trained to predict six cognitive outcomes: the Mini-Mental State Examination (MMSE) score and five Seoul Neuropsychological Screening Battery (SNSB) domain scores. Model performance was evaluated using mean squared error and Pearson’s correlation coefficient (r).</p> Results <p>The FreeSurfer-based model showed the numerically highest correlation for MMSE (<i>r</i> = 0.56), language (<i>r</i> = 0.38), visuospatial (<i>r</i> = 0.21), and frontal/executive functions (<i>r</i> = 0.32). The AQUA-based model showed the numerically highest correlation for attention (<i>r</i> = 0.40), and DeepBrain for memory (<i>r</i> = 0.38). MMSE scores were generally better predicted than domain-specific scores across all models.</p> Conclusion <p>Brain volumetry software showed modest, domain-dependent associations with cognitive scores in early AD, with the strongest signal for MMSE and no statistically significant prediction for visuospatial function. These findings support the potential of brain volumetry software to partially estimate domain-level cognitive profiles, while differences across software should be interpreted with caution rather than as evidence of intrinsic software superiority.</p>

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Comparative evaluation of automated MRI-based brain volumetry software for estimating cognitive domains in early Alzheimer’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Chae Young Lim,
  • Yongsik Sim,
  • Young-gun Lee,
  • Byoung Seok Ye,
  • Beomseok Sohn

摘要

Background

Brain volumetry software is widely accepted for assessment in Alzheimer’s disease. However, direct comparisons for software-specific prediction capabilities across cognitive domains have not been reported. This study evaluates the performance of four brain volumetry software programs in predicting domain-specific cognitive scores in patients with early Alzheimer’s disease (AD) using a consistent deep learning model.

Methods

A total of 255 patients with amyloid PET-confirmed AD were retrospectively enrolled. Brain volumetric features were extracted from 3D T1-weighted MRI using four software programs (AQUA, DeepBrain, A-finder, and FreeSurfer). A multi-layer perceptron model incorporating seven clinical variables and software-specific volumetric features was trained to predict six cognitive outcomes: the Mini-Mental State Examination (MMSE) score and five Seoul Neuropsychological Screening Battery (SNSB) domain scores. Model performance was evaluated using mean squared error and Pearson’s correlation coefficient (r).

Results

The FreeSurfer-based model showed the numerically highest correlation for MMSE (r = 0.56), language (r = 0.38), visuospatial (r = 0.21), and frontal/executive functions (r = 0.32). The AQUA-based model showed the numerically highest correlation for attention (r = 0.40), and DeepBrain for memory (r = 0.38). MMSE scores were generally better predicted than domain-specific scores across all models.

Conclusion

Brain volumetry software showed modest, domain-dependent associations with cognitive scores in early AD, with the strongest signal for MMSE and no statistically significant prediction for visuospatial function. These findings support the potential of brain volumetry software to partially estimate domain-level cognitive profiles, while differences across software should be interpreted with caution rather than as evidence of intrinsic software superiority.