Predictive Modeling of Anatomy with Genetic and Clinical Data

• Main Authors: Dalca, Adrian Vasile (Contributor), Sridharan, Ramesh (Contributor), Sabuncu, Mert R (Contributor), Golland, Polina (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Springer, 2018-06-06T15:21:45Z.
• Subjects: Article
• Online Access: Get fulltext

 We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory. Keywords: Population Trend, Baseline Image, Kernel Machine, Good Linear Unbiased Predictor, Segmentation Label