AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling

Abstract

AbstractBackgroundPredicting neurodegenerative disorders early has major implications for timely clinical management and patient outcomes. Despite advances in medical technology, we still lack tools to precisely stratify patients for targeted interventions. Here, we propose an unsupervised modelling approach based on mixtures of state space models that learns from longitudinal multimodal (brain imaging, cognitive) data to stratify patients into clusters with different clinical outcomes. Our approach has the potential to support dementia prediction based on cost‐effective, non‐invasive digital measures alone.MethodWe trained (with cross‐validation, 4 components selected with elbow method) a mixture of linear Gaussian state space models on 571 trajectories (2‐4 assessments) from ADNI comprising a) state variables: neuroimaging biomarkers (temporal lobe gray matter density, β‐amyloid) and b) observations: cognitive tests (ADNI‐Mem, ADNI‐EF, MoCA, & ADAS‐13) collected at regular 2‐year intervals. The model clustered individuals based on longitudinal changes in biomarkers and the relationship between biomarkers and cognitive scores (figure 1). We validated model‐derived clusters against clinical outcomes (unseen by the model) based on each individual’s final assessment.ResultOur unsupervised modelling approach effectively stratifies individuals by clinical outcome. Cluster A comprises 65% healthy controls and 33% stable MCI patients (individuals with MCI who do not convert to AD in a 3‐year period) while cluster D comprises 77% Alzheimer’s patients (table 1). Profiling these clusters using MMSE score validates our approach: individuals in cluster A maintain high memory scores over 6 years whereas individuals in cluster D decline (figure 2). Further, we find that our model maintains good stratification when tested a) with a single rather than multiple assessments and b) with cognitive data only (i.e. without biomarkers). These results enhance the clinical utility of our approach, as collecting longitudinal and biomarker data may prove challenging in clinical settings.ConclusionOur modelling approach robustly stratifies patients by clinical outcome when trained on longitudinal multimodal data and tested using cognitive scores alone. Our approach has strong potential to generalize to other non‐invasive and cost‐effective data (e.g. digital markers from wearable technologies), enhancing the translational impact of our AI‐guided tool for early and precise patient stratification in clinical practice.