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Related Concept Videos

Brain Imaging01:14

Brain Imaging

211
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...
211

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Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and

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Summary

This study evaluates edge computing platforms for AI-powered medical imaging diagnostics. The Android phone offers the fastest real-time performance for AI diagnostic tools, making it ideal for remote healthcare applications.

Keywords:
AndroidNvidia Jetson XavierRaspberry PiTFLiteartificial intelligence (AI)computer-aided diagnosis (CAD)cost–benefit analysismedical imagingreal-time deployment

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Edge Computing

Background:

  • Medical imaging technologies (MRI, CT, PET, ultrasound) are crucial for diagnosis.
  • Artificial Intelligence (AI) is increasingly used to enhance diagnostic radiology.
  • Edge computing offers solutions for processing medical data in resource-limited remote areas.

Purpose of the Study:

  • To evaluate the real-time deployment capabilities of various platforms for AI-driven diagnostic radiology tools.
  • To compare the performance of different hardware and software configurations for medical image analysis applications.
  • To conduct a cost-benefit analysis of deploying diagnostic tools on diverse computing platforms.

Main Methods:

  • Utilized MRI classification and segmentation applications for performance testing.
  • Evaluated platforms including a PC with NVIDIA GPU, Jetson Xavier NX, Raspberry Pi 4B, and Android phone.
  • Used MATLAB, Python, and Android Studio for development and testing.
  • Analyzed computational times and accuracy across different configurations.

Main Results:

  • The Android phone achieved the fastest classification inference time (0.1068 s) using a TFLite model, with minimal accuracy loss.
  • For segmentation, the Android phone (3.2023 s) and Jetson Xavier NX (5.2641 s) showed competitive performance.
  • The Jetson Xavier NX and Android phone were identified as optimal platforms due to their balance of size, speed, and cost.

Conclusions:

  • Edge computing platforms, particularly the Android phone and Jetson Xavier NX, are highly suitable for real-time AI diagnostic radiology applications.
  • These platforms provide efficient and affordable solutions for medical image analysis, especially in remote or underserved regions.
  • The study highlights the potential of accessible technology to advance diagnostic capabilities in healthcare.