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Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. | LitMetric

AI Article Synopsis

  • MRI is widely used in clinical practice for examining head and neck diseases due to its ability to clearly show soft tissue details.
  • Recent advancements in artificial intelligence, especially deep learning techniques like convolutional neural networks, are being explored for enhancing head and neck MRI diagnostics.
  • The review highlights the benefits of these AI methods in image processing and disease assessment, while also addressing their limitations and future challenges in clinical application.

Article Abstract

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552661PMC
http://dx.doi.org/10.2463/mrms.rev.2023-0047DOI Listing

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