{"slug": "longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s", "title": "Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum", "summary": "Researchers propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that estimates Alzheimer's disease severity continuously from diffusion tensor imaging, outperforming existing methods on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The derived Disease Continuum Score (DCS) quantifies an individual's position along the disease continuum with uncertainty, accurately characterizing severity, clinical relevance, and predicting conversion.", "body_md": "arXiv:2608.19436v1 Announce Type: new\nAbstract: Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cross-sectional imaging. In this work, we propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that continuously estimates disease severity from longitudinal diffusion tensor imaging (DTI). Specifically, DCP models disease severity as a low-dimensional probabilistic latent variable by jointly integrating longitudinal observations with weak clinical supervision, from which the proposed Disease Continuum Score (DCS) is derived to quantify an individual's position along the Alzheimer's disease continuum together with its associated uncertainty. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods. More importantly, comprehensive validation analyses show that DCS accurately characterizes disease severity, exhibits strong clinical relevance, preserves longitudinal disease evolution, and predicts future disease conversion. These results suggest that DCS provides a quantitative imaging-derived representation for continuous assessment of Alzheimer's disease progression beyond conventional diagnostic labels and clinical scores.", "url": "https://wpnews.pro/news/longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s", "canonical_source": "https://www.machinebrief.com/news/longitudinal-bayesian-learning-of-continuous-disease-positio-ciqn", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:14:02.996678+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Alzheimer's Disease Neuroimaging Initiative", "Disease Continuum Positioning", "Disease Continuum Score"], "alternates": {"html": "https://wpnews.pro/news/longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s", "markdown": "https://wpnews.pro/news/longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s.md", "text": "https://wpnews.pro/news/longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s.txt", "jsonld": "https://wpnews.pro/news/longitudinal-bayesian-learning-of-continuous-disease-position-across-the-s.jsonld"}}