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Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.

Journal of medical systems·2018
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Computational intelligence in eye disease diagnosis: a comparative study.

S V Mahesh Kumar1, R Gunasundari2

  • 1Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamil Nadu, India. maheshyesvee@gmail.com.

Medical & Biological Engineering & Computing
|January 3, 2023
PubMed
Summary

Computational intelligence (CI) aids early eye disorder detection in older adults. These AI-driven systems offer reliable diagnosis, improving clinical decisions and reducing physician workload.

Keywords:
Anterior eyeClassificationComputational intelligenceDigital imagingEye diseasesHuman eyeOphthalmologyRetinaSegmentation

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Eye disorders pose a significant health challenge, particularly for older adults, often progressing unnoticed.
  • Current diagnostic methods, like slit-lamp examinations, have limitations including subjectivity and inconsistency.
  • Digital imaging combined with computational intelligence (CI) offers a promising assistive approach for accurate eye disease diagnosis.

Purpose of the Study:

  • To present a comparative analysis of CI-based decision support models for diagnosing eye disorders.
  • To evaluate the reliability of CI systems for both anterior and retinal eye abnormalities.
  • To assess the performance of CI diagnostic systems based on precision, sensitivity, and specificity.

Main Methods:

  • Reviewed CI-based diagnostic systems for anterior and retinal eye abnormalities.
  • Discussed various eye imaging modalities, pre-processing techniques (e.g., reflection removal, contrast enhancement, segmentation), and public databases.
  • Compared the precision, sensitivity, and specificity of different CI systems for eye disorder diagnosis.

Main Results:

  • CI-based systems demonstrated significant prediction accuracy for both anterior and retinal eye disorders.
  • The comparative analysis highlighted the reliability of these AI-driven diagnostic tools.
  • The study confirmed the effectiveness of CI in enhancing diagnostic precision.

Conclusions:

  • CI-based diagnostic systems are effective tools for early detection and precise diagnosis of eye disorders.
  • These systems can reduce the burden on ophthalmologists, minimize diagnostic errors, and support clinical decision-making.
  • Implementing CI in clinical settings can lead to improved patient outcomes through timely and accurate diagnosis.