Deep Learning Aided Neuroimaging and Brain Regulation

Abstract

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.

Innovative Approaches to Detecting Neurodevelopmental Disorders

“…Machine learning techniques, including artificial intelligence algorithms, offer the potential to analyze vast amounts of data and identify patterns that may be indicative of specific neurodevelopmental conditions [8]. For brain imaging, machine learning algorithms can be employed to process imaging data and extract meaningful information about brain activity [74,75].…”

Artificial Intelligence Diagnosis of Parkinson's Disease From MRI Scans

“…In neurology, artificial intelligence (AI) has rapidly emerged as a transformative tool with diverse applications across various domains, revolutionizing the landscape of disease diagnosis, prognosis, and treatment [ 12 ]. One prominent area of AI application lies in neuroimaging analysis, where advanced machine learning algorithms are employed to interpret complex imaging data, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans [ 13 ]. These AI-driven approaches enable the detection of subtle structural and functional abnormalities indicative of neurological conditions, including Alzheimer's disease, PD, multiple sclerosis, and brain tumors, with high accuracy and efficiency [ 14 ].…”

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Editorial: Advancements of deep learning in medical imaging for neurodegenerative diseases

“…The integration of technology and healthcare, particularly through artificial intelligence and deep learning, is revolutionizing precision medicine. Advances in medical imaging using deep learning have proven effective in diagnosing and managing neurodegenerative diseases like Alzheimer's, Parkinson's, and Multiple Sclerosis (Noor et al, 2019;Myszczynska et al, 2020;Gaur et al, 2023;Ghose et al, 2023;Xu et al, 2023). Deep learning excels in detecting subtle changes in brain structure and function, providing early detection, and significantly influencing patient prognosis and treatment efficacy (Tarnanas et al, 2022;Jyotismita and Marcin, 2023;Modat et al, 2023).…”