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. 2023 Sep 18;16(9):1417-1423.
doi: 10.18240/ijo.2023.09.07. eCollection 2023.

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

Pei-Fang Ren  1 , Xu-Yuan Tang  1 , Chen-Ying Yu  1 , Li-Li Zhu  1 , Wei-Hua Yang  2 , Ye Shen  1
Affiliations

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

Pei-Fang Ren et al. Int J Ophthalmol. .

Abstract

Aim: To evaluate the clinical application value of the artificial intelligence assisted pathologic myopia (PM-AI) diagnosis model based on deep learning.

Methods: A total of 1156 readable color fundus photographs were collected and annotated based on the diagnostic criteria of Meta-pathologic myopia (PM) (2015). The PM-AI system and four eye doctors (retinal specialists 1 and 2, and ophthalmologists 1 and 2) independently evaluated the color fundus photographs to determine whether they were indicative of PM or not and the presence of myopic choroidal neovascularization (mCNV). The performance of identification for PM and mCNV by the PM-AI system and the eye doctors was compared and evaluated via the relevant statistical analysis.

Results: For PM identification, the sensitivity of the PM-AI system was 98.17%, which was comparable to specialist 1 (P=0.307), but was higher than specialist 2 and ophthalmologists 1 and 2 (P<0.001). The specificity of the PM-AI system was 93.06%, which was lower than specialists 1 and 2, but was higher than ophthalmologists 1 and 2. The PM-AI system showed the Kappa value of 0.904, while the Kappa values of specialists 1, 2 and ophthalmologists 1, 2 were 0.968, 0.916, 0.772 and 0.730, respectively. For mCNV identification, the AI system showed the sensitivity of 84.06%, which was comparable to specialists 1, 2 and ophthalmologist 2 (P>0.05), and was higher than ophthalmologist 1. The specificity of the PM-AI system was 95.31%, which was lower than specialists 1 and 2, but higher than ophthalmologists 1 and 2. The PM-AI system gave the Kappa value of 0.624, while the Kappa values of specialists 1, 2 and ophthalmologists 1 and 2 were 0.864, 0.732, 0.304 and 0.238, respectively.

Conclusion: In comparison to the senior ophthalmologists, the PM-AI system based on deep learning exhibits excellent performance in PM and mCNV identification. The effectiveness of PM-AI system is an auxiliary diagnosis tool for clinical screening of PM and mCNV.

Keywords: artificial intelligence; choroidal neovascularization; deep learning; pathologic myopia.

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Figures

Figure 1
Figure 1. Typical false positive or negative images of AI model for PM detection
A-C: False positive; A: Age-related macular degeneration; B: Multifocal choroiditis; C: Vitreous opacity; D-F: False negative (C2 was misclassified as C1). AI: Artificial intelligence; PM: Pathologic myopia.
Figure 2
Figure 2. Typical false positive or negative images of AI model for mCNV detection
A-C: False positive; A: Macular atrophy; B: Fuchs spot; C: Diabetic retinopathy; D-F: False negative (occult mCNVs). AI: Artificial intelligence; mCNV: Myopic choroidal neovascularization.
See this image and copyright information in PMC

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