Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewMachine Learning Applications for Head and Neck Imaging.
The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. ⋯ It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.
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Natural language processing (NLP) is an interdisciplinary field, combining linguistics, computer science, and artificial intelligence to enable machines to read and understand human language for meaningful purposes. Recent advancements in deep learning have begun to offer significant improvements in NLP task performance. These techniques have the potential to create new automated tools that could improve clinical workflows and unlock unstructured textual information contained in radiology and clinical reports for the development of radiology and clinical artificial intelligence applications. These applications will combine the appropriate application of classic linguistic and NLP preprocessing techniques, modern NLP techniques, and modern deep learning techniques.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewArtificial Intelligence and Stroke Imaging: A West Coast Perspective.
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. ⋯ AI techniques are well-suited for dealing with vast amounts of stroke imaging data and a large number of multidisciplinary approaches used in classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. This article addresses this topic and seeks to present an overview of machine learning and/or deep learning applied to stroke imaging.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewDiverse Applications of Artificial Intelligence in Neuroradiology.
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."
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Neuroimaging Clin. N. Am. · Nov 2020
Review Comparative StudyKnowledge Based Versus Data Based: A Historical Perspective on a Continuum of Methodologies for Medical Image Analysis.
The advent of big data and deep learning algorithms has promoted a major shift toward data-driven methods in medical image analysis recently. However, the medical image analysis field has a long and rich history inclusive of both knowledge-driven and data-driven methodologies. In the present article, we provide a historical review of an illustrative sample of medical image analysis methods and locate them along a knowledge-driven versus data-driven continuum. In doing so, we highlight the historical importance as well as current-day relevance of more traditional, knowledge-based artificial intelligence approaches and their complementarity with fully data-driven techniques such as deep learning.