Deep Learning in Neuroradiology.

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Neuroradiology

Background:

• Deep learning (DL) utilizes convolutional neural network architectures, originally from computer vision.
• DL is increasingly adapted for medical imaging due to its potential in analyzing complex datasets.
• Neuroradiology, with its high volume of multimodal imaging data, is a prime area for DL adoption.

Purpose of the Study:

• To review the reasons for deep learning adoption in neuroradiology.
• To outline the fundamental methods for training and testing DL models.
• To present current and potential clinical applications of DL in neuroradiology.

Main Methods:

• Review of existing literature on deep learning in medical imaging.
• Explanation of deep learning model training and validation processes.
• Overview of current and emerging DL applications in neuroradiology.

Main Results:

• Deep learning demonstrates compelling research applications in medical imaging.
• The use of DL in neuroradiology is expected to grow rapidly.
• DL has the potential to significantly alter future neuroradiology practice.

Conclusions:

• Familiarity with deep learning methods is crucial for neuroimaging researchers and clinicians.
• Harnessing DL's potential requires a skilled workforce.
• Deep learning is set to revolutionize neuroradiology practice.