3D Structural Phenotype of the Optic Nerve Head in Glaucoma and Myopia-A Key to Improving Glaucoma Diagnosis in Myopic Populations.
Sharma Swati, Braeu Fabian A, Chuangsuwanich Thanadet, Tun Tin A, Hoang Quan V, Chong Rachel, Perera Shamira A, Ho Ching-Lin, Husain Rahat, Buist Martin L
AI Summary
This study identified distinct 3D optic nerve head structural patterns in healthy, myopic, and glaucomatous eyes, suggesting these unique signatures can improve glaucoma diagnosis, especially in myopic patients.
Abstract
Purpose
To characterize the three-dimensional (3D) structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions.
Design
Retrospective cross-sectional study.
Participants
A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases.
Methods
We segmented the retinal and connective tissue layers from OCT volumes, and their boundary edges were converted into 3D point clouds. To classify the 3D point clouds into four ONH conditions, ie, H, HM, G, and HMG, a specialized ensemble network was developed, consisting of an encoder to transform high-dimensional input data into a compressed latent vector, a decoder to reconstruct point clouds from the latent vector, and a classifier to categorize the point clouds into the four ONH conditions. In addition, the network included an extension to reduce the latent vector to two dimensions for enhanced visualization.
Main outcome measures
Structural variation in the ONH in H, HM, G, and HMG conditions.
Results
The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a microaverage area under the receiver operating characteristic curve of 0.92 ± 0.03 on an independent test set. The decoder effectively reconstructed point clouds, achieving a Chamfer loss of 0.013 ± 0.002. Dimensionality reduction clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology, including shallow or deep excavation, across the four conditions.
Conclusions
This study demonstrated that ONHs exhibit distinct structural signatures across H, HM, G, and HMG conditions. The findings further indicate that ONH morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group. Future studies should seek to establish a connection between these structural patterns with the functional changes to enhance glaucoma diagnosis in myopic eyes.
MeSH Terms
Shields Classification
Key Concepts6
The specialized ensemble network achieved a microaverage area under the receiver operating characteristic curve of 0.92 ± 0.03 on an independent test set for distinguishing healthy (H), highly myopic (HM), glaucomatous (G), and highly myopic with glaucoma (HMG) optic nerve head (ONH) classes.
Dimensionality reduction of optic nerve head (ONH) data clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology (including shallow or deep excavation) across healthy (H), highly myopic (HM), glaucomatous (G), and highly myopic with glaucoma (HMG) conditions.
Optic nerve heads (ONHs) exhibit distinct structural signatures across healthy (H), highly myopic (HM), glaucomatous (G), and highly myopic with glaucoma (HMG) conditions.
Optic nerve head (ONH) morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group (healthy, highly myopic, glaucomatous, and highly myopic with glaucoma).
A specialized ensemble network, consisting of an encoder, a decoder, and a classifier, was developed to classify 3D point clouds of optic nerve heads (ONH) into four conditions: healthy (H), highly myopic (HM), glaucomatous (G), and highly myopic with glaucoma (HMG).
The decoder component of the specialized ensemble network effectively reconstructed 3D point clouds of optic nerve heads (ONH), achieving a Chamfer loss of 0.013 ± 0.002.
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