Work

Predictive Modeling of Anatomy with Genetic and Clinical Data

Year: 2020

Type: preprint

Abstract: 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. The code is available at https://github.com/adalca/voxelorb.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2010.04757

Authors: Dalca, Adrian V., Sridharan, Ramesh, Sabuncu, Mert R., Golland, Polina

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Harvard University, Athinoula A. Martinos Center for Biomedical Imaging

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: American Health Assistance Foundation, BrightFocus Foundation, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering