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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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Neuromorphic applications in medicine.

Khaled Aboumerhi¹, Amparo Güemes², Hongtao Liu¹

¹Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America.

Journal of Neural Engineering

|August 2, 2023

Summary

Neuromorphic engineering offers low-power, high-speed solutions for healthcare, enhancing medical imaging, biosignal processing, and prosthetics. This brain-inspired technology promises improved diagnostics and patient outcomes, overcoming current integration challenges.

Keywords:

medical imaging medicine neuromodulation neuromorphic engineering neuroprostheses

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Area of Science:

Biomedical Engineering
Neuroscience
Computer Science

Background:

Growing demand for miniaturized, low-power, non-invasive medical technologies.
Need for improved diagnostic accuracy and patient compliance in healthcare.
Emergence of neuromorphic engineering as a solution for advanced medical applications.

Purpose of the Study:

To provide an overview of neuromorphic engineering advancements in medicine.
To highlight the potential of brain-inspired models in medical applications.
To discuss challenges and solutions for integrating neuromorphic hardware.

Main Methods:

Review of recent neuromorphic engineering applications in healthcare.
Comparative analysis of neuromorphic models versus conventional AI algorithms.
Exploration of medical imaging, biosignal processing, and biomedical interfaces.

Main Results:

Neuromorphic engineering demonstrates potential in medical imaging, cancer diagnosis, and biosignal processing.
Brain-inspired models show competitive performance against traditional AI.
Advancements in motor, cognitive, and perception prosthetics using neuromorphic approaches.

Conclusions:

Neuromorphic engineering is poised to revolutionize medicine with its efficient, brain-like processing.
Successful integration can lead to improved diagnostic accuracy and patient outcomes.
Addressing hardware compatibility is crucial for future widespread adoption.