Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.
Bowd Christopher, Belghith Akram, Rezapour Jasmin, Christopher Mark, Hyman Leslie, Jonas Jost B, Weinreb Robert N, Zangwill Linda M
AI Summary
A novel OCT texture analysis (SALSA-Texture) improved glaucoma detection in high myopes compared to traditional thickness maps, offering a promising tool where standard segmentation is challenging.
Abstract
Purpose
To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell-inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps.
Design
Comparison of diagnostic approaches.
Methods
Cross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-μm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches.
Results
The best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P ≤ .02 compared with texture for all comparisons).
Conclusion
The current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.
MeSH Terms
Shields Classification
Key Concepts4
The best parameter-adjusted AUROC (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes was 0.92 (0.88-0.94) for texture en face images, compared to 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness, in a cross-sectional study of 92 eyes with POAG and 44 healthy control eyes with axial high myopia (axial length >26 mm).
In a subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes), the best AUROC for detecting glaucoma was 0.92 (0.89-0.94) for texture en face images, which was significantly better (P ≤ .02 for all comparisons) compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness.
The novel en face texture-based analysis method, SALSA-Texture, can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes, suggesting promise for improving glaucoma detection in eyes with high myopia where traditional retinal layer segmentation is often challenging.
A novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) was developed to model the spatial arrangement patterns of pixel intensities in a region, generated from 70-μm slabs just below the vitreal border of the inner limiting membrane, requiring segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia.
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