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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

Kunal Aggarwal1,2, Marina Manso Jimeno3,4, Keerthi Sravan Ravi3,4

  • 1Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Department of Diagnostic, Molecular and Interventional Radiology, Mount Sinai Hospital, New York, USA.

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Summary

Deep learning (DL) enhances brain MRI, but clinical deployment lags. This review outlines DL development and deployment guidelines for accessible neuroimaging, focusing on best practices and regulatory considerations.

Keywords:
explainable artificial intelligencegood machine learning practicesimage acquisitionimage reconstructionmachine learning for accessible MRIneuroimagingprerequisites for deep learning-based MRIradiological reporting

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) shows promise for improving brain MRI efficiency and accessibility.
  • Despite numerous DL models and publications, clinical adoption in neuroimaging remains limited.
  • Accreditation agencies' guidelines are crucial for bridging the gap between DL research and clinical practice.

Approach:

  • This review synthesizes current knowledge on DL for brain MRI, from data acquisition to clinical deployment.
  • It examines DL applications in neuropathology and collates essential prerequisites for model development.
  • The review emphasizes good machine learning practices, including explainability, and provides a checklist based on FDA guidelines.

Key Points:

  • DL offers significant potential to streamline brain MRI workflows and enhance diagnostic capabilities.
  • Successful clinical integration requires adherence to rigorous development, validation, and regulatory standards.
  • Explainability and robust machine learning practices are paramount for building trust and ensuring patient safety in neuroimaging AI.

Conclusions:

  • Bridging the gap between DL research and clinical neuroimaging requires a comprehensive approach to development and deployment.
  • Adherence to established guidelines and best practices is essential for the responsible and effective implementation of AI in brain MRI.
  • Addressing current challenges and exploring future opportunities will further unlock the potential of DL for accessible and advanced neuroimaging.