ArticleBMC medical imaging2026
Comparative evaluation of automated MRI-based brain volumetry software for estimating cognitive domains in early Alzheimer's disease.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundBrain 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.
methodsA 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).
resultsThe 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.
conclusionBrain 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.