Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.

Area of Science:

• Neuroimaging
• Artificial Intelligence
• Medical Image Analysis

Background:

• Cognitive impairments from stroke, Alzheimer's, and Parkinson's disease involve distinct brain alterations.
• Brain Magnetic Resonance Imaging (MRI) provides high-resolution, non-invasive assessment of these changes.
• Deep learning (DL) offers advanced tools for automated analysis of complex neuroimaging data.

Purpose of the Study:

• To review the application of deep learning (DL) techniques in brain MRI analysis for cognitive impairments.
• Focus on three core tasks: lesion segmentation, object detection, and image classification.
• Highlight DL's role in understanding structural and network-level alterations in neurological disorders.

Main Methods:

• Review of recent findings on DL models applied to brain MRI for ischemic stroke, Alzheimer's, and Parkinson's disease.
• Analysis of DL techniques including U-Net, Convolutional Neural Networks (CNNs), Transformers, and multimodal fusion.
• Evaluation of model performance in lesion segmentation, classification, and detection of subtle abnormalities.

Main Results:

• State-of-the-art lesion segmentation in stroke using U-Net and hybrid models (Dice scores up to 0.911).
• Improved classification and staging accuracy for Alzheimer's disease using 3D CNNs and multimodal fusion.
• Identification of subtle abnormalities in Parkinson's disease using ResNet and Vision Transformers for early differentiation.

Conclusions:

• Deep learning significantly enhances the accuracy and robustness of brain MRI analysis for cognitive impairments.
• Challenges include data scarcity, annotation costs, inter-site variability, and limited interpretability.
• Future directions involve federated learning, domain adaptation, explainable AI, and clinical workflow integration.