Age Prediction Based on Brain MRI Image: A Survey.

Area of Science:

• Medical Imaging
• Machine Learning
• Neuroscience

Background:

• Human age prediction is vital across disciplines, with image-based methods gaining prominence.
• Brain Age Estimation (BAE) using MRI aids in early diagnosis of neurodegenerative diseases like Alzheimer's.
• Accelerated brain aging correlates with brain atrophy, indicating disease effects.

Purpose of the Study:

• To review and summarize current approaches for age prediction using brain MRI.
• To categorize Brain Age Estimation methods based on image processing techniques and machine learning algorithms.
• To discuss challenges and suggest future research directions in MRI-based age prediction.

Main Methods:

• Categorization of BAE methods into pixel-based, surface-based, and voxel-based approaches.
• Classification of machine learning algorithms into traditional and Deep Learning (DL) methods.
• Review of preprocessing techniques, tools, and estimation algorithms used in BAE research.

Main Results:

• Deep Learning (DL) methods enhance the accuracy of MRI-based age prediction.
• Precise statistical Machine Learning (ML) approaches with specialized tools improve computational efficiency and results.
• The study identifies pros, cons, and challenges associated with various BAE research methodologies.

Conclusions:

• MRI-based Brain Age Estimation is a promising tool for medical applications, particularly in neurodegenerative disease diagnosis.
• Advanced ML and DL techniques are key to achieving more accurate age predictions.
• Further research is needed to address existing challenges and refine BAE methodologies.