research
The Scan Moved Beyond a Diagnosis Box
A Bayesian model placed longitudinal brain images on a continuous Alzheimer’s disease trajectory with explicit uncertainty.
Summary
A Bayesian model placed longitudinal brain images on a continuous Alzheimer’s disease trajectory with explicit uncertainty.
Disease Continuum Positioning integrates longitudinal diffusion-tensor imaging with weak clinical supervision to produce a probabilistic Disease Continuum Score. On the ADNI cohort, the authors report stronger performance than comparison progression methods, longitudinal consistency and predictive value for future conversion. The result is retrospective modeling on a research cohort, not a clinical diagnosis or validation for individual care.
Why it matters
A Bayesian model placed longitudinal brain images on a continuous Alzheimer’s disease trajectory with explicit uncertainty.
Limits and context
- The result is retrospective modeling on a research cohort, not a clinical diagnosis or validation for individual care.
Key claims
A Bayesian model placed longitudinal brain images on a continuous Alzheimer’s disease trajectory with explicit uncertainty.
Qualification: The result is retrospective modeling on a research cohort, not a clinical diagnosis or validation for individual care.
Evidence: source-2026-08-21-004
Sources
- arXiv preprint 2608.19436arXiv · primary research
Corrections
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