About this Research Topic
Quantitative analysis and artificial intelligence are evolving rapidly in the field of medicine and has begun to have an impact on how head and neck imaging is conducted and interpreted. Thus, being familiar with artificial intelligence and its utility is important. This Research Topic will showcase the role of radiomics and artificial intelligence in head and neck imaging, including how these are performed and how it's applied in clinical practice. In particular, radiomics, machine learning, and deep learning algorithms will be reviewed as pertains to neuroimaging applications, such as optimizing workflow, quality assurance, image segmentation, diagnosis, and treatment response prediction. This will be based on the current research literature and future directions will also be discussed. Illustrative examples will be included.
The goal of this Research Topic is to exhibit novel approaches and advances related to quantitative analysis and artificial intelligence for head and neck imaging and the potential impact on clinical care and future research. These techniques could revolutionize radiology and this series of articles are intended to familiarize the reader with advances in this realm.
We welcome manuscripts with topics that focus on head and neck imaging including, but not limited
to:
• Radiomics
• Radiogenomics
• Segmentation of normal anatomy and lesions
• Workflow optimization
• Prognosis
• Determining the most suitable deep-learning algorithms and exploration of recent methods, such as general AI models and transformers, for image analysis.
Both original research articles and review articles are suitable for this Research Topic. Articles
describing real clinical applications, especially using large datasets, are especially sought.
The goal of this Research Topic is to exhibit novel approaches and advances related to quantitative analysis and artificial intelligence for head and neck imaging and the potential impact on clinical care and future research. These techniques could revolutionize radiology and this series of articles are intended to familiarize the reader with advances in this realm.
We welcome manuscripts with topics that focus on head and neck imaging including, but not limited
to:
• Radiomics
• Radiogenomics
• Segmentation of normal anatomy and lesions
• Workflow optimization
• Prognosis
• Determining the most suitable deep-learning algorithms and exploration of recent methods, such as general AI models and transformers, for image analysis.
Both original research articles and review articles are suitable for this Research Topic. Articles
describing real clinical applications, especially using large datasets, are especially sought.
Keywords: radiology, imaging, neuroimaging, artificial intelligence, deep learning, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
