A Review of Mixed-Effect Modeling in the Longitudinal Studies Using Medical Images of Patients

TL;DR

This review explores the application of mixed-effect models in analyzing longitudinal medical images, highlighting their structure, design choices, and examples in Alzheimer's and prostate studies.

Contribution

It provides a comprehensive overview of mixed-effect modeling frameworks and discusses their application in longitudinal medical image analysis.

Findings

Identified key elements and design choices in mixed-effect models.

Illustrated applications with Alzheimer’s and prostate imaging studies.

Highlighted the importance of model structure in medical image analysis.

Abstract

In this review paper, some applications of the mixed effect modeling in medial image processing and longitudinal analysis is studied. For this purpose, a general structure is extracted from some of the researches in the literature. This structure includes a number of essential elements, each of which having a few design choices, namely 1) tracked features, 2) models mathematical expression and random effects and finally 3) response prediction. Two research study examples in Alzheimers disease and prostate tomography are also briefly introduced to further discuss the above design choices.