Neuroimaging has provided valuable insights into the structures and mechanisms of the brain, advancing our understanding of neurological diseases. In recent years, deep learning, a subset of artificial intelligence, has shown potential in further enhancing these insights by capable of handling complex data and revealing previously undetected patterns in neuroimaging. However, fully integrating deep learning into the neuroimaging process and applying it to neurological diseases need further investigation.
The primary objective of this Research Topic is to highlight and address the potential of deep learning techniques in the analysis and interpretation of neuroimaging data concerning neurological diseases. We aim to focus on the innovation and optimization of these techniques, their application in various neurological diseases, and the challenges and solutions encountered during their implementation. The ultimate goal is to pave the way for better diagnosis, prognosis, and treatment strategies.
We welcome original research, perspectives, and review articles focused on, but not limited to: development of deep learning algorithms for neuroimage processing; applications of these algorithms in studying neurological diseases like Alzheimer's, Parkinson's, and Multiple Sclerosis; addressing challenges in integrating deep learning into current clinical practice; evaluations of the diagnostic and predictive accuracy of these techniques. Furthermore, studies that provide a better understanding of the principles and pitfalls of using deep learning in neuroimaging are highly encouraged. In essence, this topic aims to promote an interdisciplinary dialogue to comprehensively understand and maximize the benefits of deep learning in neuroimaging for neurological diseases. Authors are encouraged to submit impactful and innovative studies to bridge the gap between computational neuroscience and clinical applications.
Neuroimaging has provided valuable insights into the structures and mechanisms of the brain, advancing our understanding of neurological diseases. In recent years, deep learning, a subset of artificial intelligence, has shown potential in further enhancing these insights by capable of handling complex data and revealing previously undetected patterns in neuroimaging. However, fully integrating deep learning into the neuroimaging process and applying it to neurological diseases need further investigation.
The primary objective of this Research Topic is to highlight and address the potential of deep learning techniques in the analysis and interpretation of neuroimaging data concerning neurological diseases. We aim to focus on the innovation and optimization of these techniques, their application in various neurological diseases, and the challenges and solutions encountered during their implementation. The ultimate goal is to pave the way for better diagnosis, prognosis, and treatment strategies.
We welcome original research, perspectives, and review articles focused on, but not limited to: development of deep learning algorithms for neuroimage processing; applications of these algorithms in studying neurological diseases like Alzheimer's, Parkinson's, and Multiple Sclerosis; addressing challenges in integrating deep learning into current clinical practice; evaluations of the diagnostic and predictive accuracy of these techniques. Furthermore, studies that provide a better understanding of the principles and pitfalls of using deep learning in neuroimaging are highly encouraged. In essence, this topic aims to promote an interdisciplinary dialogue to comprehensively understand and maximize the benefits of deep learning in neuroimaging for neurological diseases. Authors are encouraged to submit impactful and innovative studies to bridge the gap between computational neuroscience and clinical applications.
