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Evaluation of a novel deep learning based screening system for pathologic myopia.

Pei-Fang Ren1, Xu-Yuan Tang1, Chen-Ying Yu1

  • 1Department of Ophthalmology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang Province, China.

International Journal of Ophthalmology
|September 19, 2023
PubMed
Summary

A deep learning-based artificial intelligence system demonstrates high accuracy in identifying pathologic myopia (PM) and myopic choroidal neovascularization (mCNV). This AI tool shows promise as an auxiliary diagnostic aid for clinical screening.

Keywords:
artificial intelligencechoroidal neovascularizationdeep learningpathologic myopia

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pathologic myopia (PM) is a leading cause of vision impairment.
  • Myopic choroidal neovascularization (mCNV) is a severe complication of PM.
  • Accurate and timely diagnosis of PM and mCNV is crucial for effective management.

Purpose of the Study:

  • To evaluate the clinical utility of a deep learning-based artificial intelligence (AI) model for diagnosing PM.
  • To assess the AI model's capability in identifying mCNV.
  • To compare the AI model's performance against experienced ophthalmologists.

Main Methods:

  • A dataset of 1156 color fundus photographs was curated and annotated for PM.
  • An AI system (PM-AI) and four ophthalmologists independently analyzed the images.
  • Performance metrics including sensitivity, specificity, and Kappa values were calculated and compared.

Main Results:

  • The PM-AI system achieved high sensitivity (98.17%) and specificity (93.06%) for PM identification, comparable to or exceeding human experts.
  • For mCNV detection, the AI system demonstrated sensitivity of 84.06% and specificity of 95.31%.
  • The AI system's Kappa values for both PM and mCNV indicated substantial agreement, similar to senior specialists.

Conclusions:

  • The deep learning-based PM-AI system shows excellent performance in identifying PM and mCNV.
  • The AI system serves as a valuable auxiliary diagnostic tool for clinical screening of PM and mCNV.
  • AI-assisted diagnosis can potentially enhance the efficiency and accuracy of ophthalmological assessments.