Agreement and Comparative Performance of Cognitive Testing, Visual MRI Rating, and Automated Brain Morphometry in...

Dementia diagnosis in sub-Saharan Africa is constrained by limited access to specialist neuroimaging interpretation and reduced specificity of brief cognitive tools in low-literacy populations. We evaluated the agreement, incremental value, and comparative performance of Mini Mental State Exam (MMSE), visual MRI medial temporal atrophy (MTA), and automated brain morphometry in older Ugandan adults with suspected dementia.

In this cross-sectional study, adults aged ≥50 years with suspected dementia were recruited from neurology and psychiatry clinics at two hospitals and from a community cohort. Participants underwent MMSE and standardized 1.5T brain MRI. Visual MRI ratings were performed by radiologists blinded to clinical data, and automated morphometry was generated using NeuroQuant^® normative percentiles. Hippocampal occupancy (HOC <5th percentile) was used as a reference MRI biomarker for comparative classification. Agreement between visual and automated measures was assessed using Spearman correlation and intraclass correlation. Incremental value was assessed using regression models, and comparative performance using area under the curve (AUC).

Sixty-three participants were included (mean age 75.6 ± 8.7 years; 49 female). Agreement between visual ratings and automated morphometry was poor. MMSE correlated inversely with MTA (ρ = -0.47; p = 0.049) and correlated positively with hippocampal volume percentile (ρ = 0.46; p = 0.056). Adding hippocampal volume to MTA did not improve model fit for MMSE (ΔR² = 0.028; p = 0.18). For comparative classification, MMSE alone was sensitive but poorly specific, while the combined MMSE-MTA model improved specificity and discrimination (AUC 0.70 vs 0.62 for either measure alone).

Visual and automated MRI measures were not interchangeable in this heterogeneous cohort. Automated hippocampal volumetry added limited value beyond visual MTA for global cognition, while combining MMSE with visual MTA showed modest improvement in comparative classification and warrants further validation.