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Brain Imaging

Brain Imaging

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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How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Nalini M Singh¹, Jordan B Harrod¹, Sandya Subramanian¹

¹Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Neuroinformatics

|March 29, 2022

Summary

This summary is machine-generated.

Machine learning is revolutionizing neuroimaging for earlier disease detection and treatment. This technology aims to improve brain health outcomes by integrating advanced imaging analysis into clinical practice.

Keywords:

Brain health Clinical translational neuroimaging Deep learning EEG MRI Machine learning PET Transcranial magnetic stimulation

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Area of Science:

Medical Imaging
Machine Learning
Neuroscience

Background:

Clinical translational imaging is crucial for brain health.
Machine learning (ML) offers transformative potential for neuroimaging.
Bridging the gap between research and clinical application is essential.

Purpose of the Study:

To provide an overview of ML advancements in clinical translational imaging.
To highlight ML's role in early disease detection, prediction, and treatment.
To discuss technical, ethical, and regulatory challenges in ML neuroimaging.

Main Methods:

Information synthesis from a symposium on neuroimaging indicators.
Exploration of ML applications on large-scale neuroimaging datasets.
Discussion of challenges from image formation to clinical workflow integration.

Main Results:

ML is rapidly advancing neuroimaging for brain health.
ML can transform healthcare delivery and disease trajectory.
Addressing brain care earlier in life is a key focus.

Conclusions:

ML holds significant promise for maintaining brain health.
Overcoming technical and ethical challenges is vital for implementation.
Inspiring future research in ML-driven neuroimaging is encouraged.