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A Novel Grading System for Diffuse Chorioretinal Atrophy in Pathologic Myopia.

Yu-Ning Niu¹, Hai-Long He¹, Xuan-Yu Chen²

¹Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China.

Ophthalmology and Therapy

|March 5, 2024

Summary

This summary is machine-generated.

A new AI-driven grading system accurately classifies diffuse chorioretinal atrophy (DCA) severity in pathologic myopia based on lesion area and density. This standardized approach aids in evaluating disease progression and screening for high myopia.

Keywords:

Artificial intelligence Diffuse chorioretinal atrophy Pathologic myopia

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Pathologic myopia is characterized by progressive axial elongation and retinal changes.
Diffuse chorioretinal atrophy (DCA) is a significant complication, impacting visual function.
Quantitative assessment and standardized classification of DCA are crucial for management.

Purpose of the Study:

To quantitatively assess diffuse chorioretinal atrophy (DCA) in pathologic myopia.
To develop and validate a standardized classification system for DCA using artificial intelligence.
To correlate DCA grades with clinical and imaging parameters.

Main Methods:

Utilized deep learning segmentation models for image analysis of 338 eyes from 202 patients.
Quantified DCA lesion area and density.
Employed hierarchical clustering to establish a four-grade classification system (G0-G3) based on area and density.
Performed statistical analyses (ANOVA) to assess associations with clinical parameters.

Main Results:

A four-grade system (G0-G3) was established based on DCA area and density.
DCA lesions showed progressive enlargement, altered morphology, and color changes with increasing grades.
Patients with DCA were older, had longer axial lengths, and more myopic spherical equivalents compared to controls (G0).
Higher DCA grades (G1-G3) correlated significantly with older age, longer axial length, increased myopia, larger parapapillary atrophy, and fundus tessellation.

Conclusions:

The AI-based grading system provides a reliable method for assessing DCA severity in pathologic myopia.
This standardized classification is suitable for widespread application in screening and managing high myopia.
The system facilitates objective evaluation of disease progression and associated risk factors.