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Artificial Intelligence Segmentation Algorithm-Based Optical Coherence Tomography Image in Evaluation of Binocular

Jiemei Shen1

  • 1Department of Ophthalmology, The First People's Hospital of Tonglu County, Tonglu, 311500 Hangzhou, China.

Computational and Mathematical Methods in Medicine
|June 13, 2022
PubMed
Summary

This study shows that using an intelligent segmentation algorithm with optical coherence tomography (OCT) images significantly improves treatment effectiveness for retinopathy patients compared to conventional methods. The algorithm enhances corneal thickness measurement accuracy and anti-interference capabilities.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Retinopathy treatment efficacy can be limited by conventional imaging techniques.
  • Accurate corneal thickness measurement is crucial for assessing and managing eye conditions.

Purpose of the Study:

  • To investigate the clinical efficacy and safety of docetaxel combined with fluorouracil using optical coherence tomography (OCT) with an intelligent segmentation algorithm.
  • To evaluate the performance of an intelligent segmentation algorithm for feature extraction and corneal thickness measurement in OCT images.

Main Methods:

  • A study involving 60 retinopathy patients, divided into a control group (conventional images) and an observation group (algorithm-based OCT images).
  • Development and application of an intelligent segmentation boundary detection algorithm, boundary tracking, and contour localization for OCT image analysis.
  • Measurement of corneal thickness in OCT images, considering variations in signal-to-noise ratio, noise, and artifacts.

Main Results:

  • The observation group, utilizing algorithm-based OCT images, demonstrated a total effective rate of 96.67%, significantly higher than the control group's 80%.
  • The algorithm-based approach showed significantly better curative effects and physical improvement rates (P < 0.05).
  • The intelligent segmentation method exhibited strong anti-interference ability and high measurement accuracy, with average thicknesses of 562.7 μm for high-quality images and 573.8 μm for images with noise and artifacts.

Conclusions:

  • The proposed feature extraction and corneal measurement method using OCT images with an intelligent segmentation algorithm is effective.
  • This approach offers advantages in terms of anti-interference and measurement accuracy for clinical applications in ophthalmology.