Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation

TL;DR

This paper introduces Age-Net, an MRI-based iterative framework for estimating brain biological age, addressing the challenge of defining biological age and differentiating organ-specific aging patterns, with promising results in Alzheimer's disease analysis.

Contribution

The paper presents a novel deep learning framework with an iterative data-cleaning algorithm for brain biological age estimation using MRI, improving upon existing methods and enabling organ-specific aging insights.

Findings

Age-Net accurately estimates chronological age from brain MRI.

Iterative cleaning segregates atypical aging patients effectively.

Predicted biological age correlates with cognitive decline in Alzheimer's.

Abstract

The concept of biological age (BA), although important in clinical practice, is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study, we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA $\not \approx$ CA) from the given population. We hypothesize that the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.