[Paper Review] Deep Learning for Brain Age Estimation: A Systematic Review

M. Tanveer, M. A. Ganaie+8 more|arXiv (Cornell University)|Dec 7, 2022

Functional Brain Connectivity Studies7 citations

TL;DR

This systematic review synthesizes deep learning approaches for brain age estimation using neuroimaging data, analyzing architectures, frameworks, and performance across studies. It identifies key trends, evaluates model robustness, uncertainty, and explainability, and outlines future research directions for clinical deployment.

ABSTRACT

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models

Motivation & Objective

• To provide a comprehensive, systematic analysis of deep learning applications in brain age estimation using neuroimaging data.
• To evaluate the strengths and limitations of various deep learning architectures and frameworks used in brain age prediction.
• To examine the role of uncertainty estimation and model explainability in enhancing clinical trustworthiness of brain age models.
• To identify research gaps and propose future directions for improving accuracy, robustness, and clinical applicability of deep learning-based brain age estimation.
• To serve as a foundational reference for researchers new to or experienced in the field of deep learning for neuroimaging and brain aging.

Proposed method

• Conducted a systematic literature review of peer-reviewed studies on deep learning for brain age estimation using neuroimaging (primarily MRI).
• Classified and analyzed deep learning architectures (e.g., 3D CNNs, autoencoders, residual networks, Transformers) based on their design and performance.
• Evaluated brain age estimation frameworks using supervised regression with training on healthy subjects and testing on independent data.
• Assessed uncertainty estimation techniques such as Monte Carlo dropout, evidential deep learning, and conformal prediction to quantify model confidence.
• Reviewed post-hoc explainability methods like LIME and SHAP to interpret model predictions and identify relevant brain regions.
• Proposed a taxonomy to organize the literature based on model architecture, data modality, performance metrics, and clinical relevance.

Experimental results

Research questions

• RQ1 Which deep learning architectures are most effective for brain age estimation, and how do they compare in performance and generalizability?
• RQ2 How do uncertainty estimation techniques improve the reliability and clinical usability of brain age predictions?
• RQ3 To what extent do explainability methods enhance transparency and trust in deep learning models for brain age estimation?
• RQ4 What are the key limitations and biases present in current deep learning-based brain age estimation frameworks?
• RQ5 What future research directions are needed to advance the clinical translation of deep learning models in brain age estimation?

Key findings

• Deep learning models, particularly 3D convolutional neural networks and residual networks, consistently outperform traditional machine learning methods in brain age estimation accuracy.
• Uncertainty estimation techniques such as Monte Carlo dropout and evidential deep learning significantly improve model reliability by providing confidence intervals for predictions.
• Post-hoc explainability methods like LIME and SHAP have been successfully applied to identify brain regions most predictive of age, offering potential biomarkers for brain aging.
• Despite high performance, many models lack interpretability and uncertainty quantification, limiting their clinical adoption.
• A significant proportion of studies use small, non-representative datasets, raising concerns about model generalizability and bias.
• Future research should prioritize model transparency, uncertainty quantification, and integration with clinical decision-making systems to enhance real-world applicability.