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We present a semi-parametric generative model for predicting anatomy of apatient in subsequent scans following a single baseline image. Such predictivemodeling promises to facilitate novel analyses in both voxel-level studies andlongitudinal biomarker evaluation. We capture anatomical change through acombination of population-wide regression and a non-parametric model of thesubject's health based on individual genetic and clinical indicators. Incontrast to classical correlation and longitudinal analysis, we focus onpredicting new observations from a single subject observation. We demonstrateprediction of follow-up anatomical scans in the ADNI cohort, and illustrate anovel analysis approach that compares a patient's scans to the predictedsubject-specific healthy anatomical trajectory. The code is available athttps://github.com/adalca/voxelorb.

Predictive Modeling of Anatomy with Genetic and Clinical Data