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Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and
Akram Belghith1, Christopher Bowd1, Robert N Weinreb1
1Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, The Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, USA.
Purpose:
To evaluate the accuracy of a three-dimensional (3D) deep learning (3D DL) and 3D cross domain deep learning (3D CD-DL) classifiers compared to standard macular ganglion cell-inner plexiform layer (GCIPL) thickness measurements for classifying eyes with glaucoma using optical coherence tomography (OCT).
Methods:
A total of 502 primary open-angle glaucoma eyes from 295 patients and 119 healthy eyes from 63 individuals were included. Two classifiers were compared: (1) a 3D DL model trained on Spectralis macular OCT and applied to Spectralis macular OCT images and (2) 3D CD-DL model trained on synthetic Spectralis images generated from 3D Cirrus macular OCT using Cycle-consistent adversarial networks (CycleGAN) and applied to real Spectralis macula OCT images. An additional 100 different eyes (50 Cirrus, 50 Spectralis) were used to train the CycleGAN. Age, axial length, and disc area adjusted area under the receiver operating curves (AUROC) were used to compare model accuracy.
Results:
Adjusted AUROC for 3D DL model was 0.92 (95% confidence interval [CI], 0.85-0.95). This was significantly higher than global GCIPL thickness (0.83 [0.78-0.85], p ≤ 0.001) but similar to 3D CD-DL (0.91 [0.84-0.95], P = 0.45). Using only early glaucoma eyes (mean deviation ≥ -3.0 dB), the 3D DL model showed significantly higher diagnostic accuracy (0.90 [0.84-0.94]) compared to global GCIPL thickness (0.80 [0.76-0.82], P ≤ 0.001) but similar to the 3D CD-DL model (0.90 [0.83-0.93], P = 0.51).
Conclusions:
The 3D DL classifier showed significantly higher diagnostic accuracy than global GCIPL thickness but was similar in performance to the 3D CD-DL classifier. By using synthetic data and diverse training sets, cross-domain learning produces robust, generalizable models across different imaging devices as demonstrated by the comparable accuracy of the 3D CD-DL and device-specific 3D DL models. More data from other OCT devices are needed to further validate these findings.
Translational Relevance:
The 3D Deep learning models significantly surpass traditional GCIPL thickness measurements for accurately detecting glaucoma. The cross-domain model closely matches the performance of the device-specific model in glaucoma classification potentially reducing the need for device-specific models in clinical practice.
