Artificial Intelligence in Neuroimaging: Clinical Applications
ÃÖ±Ô¼º, Sunwoo Leonard,
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ÃÖ±Ô¼º ( Choi Kyu-Sung )
Seoul National University Hospital Department of Radiology
( Sunwoo Leonard )
Seoul National University Bundang Hospital Department of Radiology
Abstract
Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.
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Artificial intelligence; Deep learning; Radiomics; Neuroimaging; Clinical application
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