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Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local

Yao-Wen Yu1, Cheng-Hung Lin1, Cheng-Kai Lu2

  • 1Department of Electrical Engineering, Yuan Ze University, Taoyuan City 320, Taiwan.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated artificial intelligence chip for detecting age-related macular degeneration (AMD) using 3D optical coherence tomography (OCT) scans. The novel method significantly reduces computational load, improving diagnostic efficiency for ophthalmologists.

Keywords:
age-related macular degeneration (AMD)application-specific integrated circuit (ASIC)artificial intelligence (AI)optical coherence tomography (OCT)slice-sum

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Age-related macular degeneration (AMD) diagnosis is time-consuming.
  • Current diagnostic methods for AMD require significant physician time.
  • Advancements in artificial intelligence (AI) offer potential for automating medical diagnostics.

Purpose of the Study:

  • To develop an automated detector chip for AMD.
  • To reduce the time and computational complexity of AMD diagnosis.
  • To assist ophthalmologists in faster and more accurate AMD detection.

Main Methods:

  • Utilized a support vector machine (SVM) algorithm with 3D optical coherence tomography (OCT) volume data.
  • Proposed a novel 'slice-sum' feature vector connection method to reduce computational complexity.
  • Employed local binary patterns for feature extraction, optimized for hardware efficiency.
  • Designed the detector using TSMC 40 nm CMOS technology.

Main Results:

  • Achieved a significant reduction in computational complexity (over 100-fold) compared to previous methods.
  • The detector chip has a core area of 0.12 mm² and operates at 454.54 MHz.
  • Demonstrated a classification throughput of 8.87 decisions/s.
  • Attained a final classification accuracy of 92.31% in testing.

Conclusions:

  • The proposed automated detector chip offers a highly efficient and accurate solution for AMD detection.
  • The 'slice-sum' method significantly reduces computational complexity, making AI-based AMD diagnosis more feasible.
  • The design's modularity allows for future model updates and adaptability to similar 3D imaging datasets.