Expert Commentary:
The integration of machine learning in the field of medicine has revolutionized diagnostic precision, particularly in the interpretation of complex structures such as the human brain. Brain age estimation techniques have emerged as a valuable tool for diagnosing challenging conditions like Alzheimer’s disease. These techniques heavily rely on three-dimensional Magnetic Resonance Imaging (MRI) scans, and recent studies have highlighted the effectiveness of 3D convolutional neural networks (CNNs) like 3D ResNet.
However, the untapped potential of Vision Transformers (ViTs) in this domain has been limited by the absence of efficient 3D versions. Vision Transformers are well-known for their accuracy and interpretability in various computer vision tasks, but their application to brain age estimation has been hindered by this limitation.
In this paper, the authors propose an innovative adaptation of the ViT model called Triamese-ViT to address the limitations of current approaches. Triamese-ViT combines ViTs from three different orientations to capture 3D information, significantly enhancing accuracy and interpretability. The experimental results on a dataset of 1351 MRI scans demonstrate Triamese-ViT’s superiority over previous methods for brain age estimation, achieved through a Mean Absolute Error (MAE) of 3.84 and strong correlation coefficients with chronological age.
One key innovation introduced by Triamese-ViT is its ability to generate a comprehensive 3D-like attention map synthesized from 2D attention maps of each orientation-specific ViT. This feature brings significant benefits in terms of in-depth brain age analysis and disease diagnosis, offering deeper insights into brain health and the mechanisms of age-related neural changes.
The development of Triamese-ViT marks a crucial step forward in the field of brain age estimation using machine learning techniques. By leveraging the strengths of ViTs and incorporating 3D information, this model has the potential to greatly improve accuracy and interpretability in diagnosing age-related neurodegenerative disorders. Further research should explore the generalizability of the Triamese-ViT model across larger and more diverse datasets, as well as its applicability to other medical imaging tasks beyond brain age estimation.