Quantifying White Matter Hyperintensities: Automated Volumetry Compared with Visual Grading Scales
Bibliographic record
Abstract
Background and objectives. White matter hyperintensities (WMHs) on brain magnetic resonance imaging (MRI) are linked to cognitive decline, but clinical assessment still relies mainly on visual grading (Fazekas), which is coarse and rater-dependent. We described the lesion volume of WMHs and the association of the anatomical distribution with the severity of cognitive impairment using automated lesion analysis. In addition, we evaluated whether automated volumetric quantification is more strongly associated with cognitive performance than visual grading. Materials and Methods. In a retrospective cross-sectional study, forty-one adults referred for cognitive concerns underwent standardised 3.0 tesla MRI. White matter hyperintensities were automatically segmented using Icometrix software to obtain total and regional volumes (periventricular, subcortical, brainstem, cerebellum). Visual grading used the Fazekas scale separately for periventricular and deep white matter, with a combined grade defined by the higher of the two. Cognitive performance was grouped based on the Montreal Cognitive Assessment (MoCA) into high (≥26), moderate (18–25), and low (≤17). Statistics included Spearman’s correlation and the Kruskal–Wallis test with Dunn’s post hoc test where applicable. Results. Higher total white matter hyperintensity volume was associated with lower Montreal Cognitive Assessment scores and showed significant differences across cognitive groups. The Fazekas combined grade correlated more weakly with the MoCA score. Regional volumetric differences showed trends, but were not statistically significant. Total volumetric burden increased stepwise across combined Fazekas categories, supporting convergent validity between methods. Conclusions. Our study found that automated volumetric quantification provides a more objective, sensitive, and scalable measure of white matter hyperintensity burden than visual grading, aligns more closely with cognitive status, and is better suited for longitudinal monitoring and research endpoints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".