Abstract
Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.
Highlights
Through the various scan protocols provided by Optical coherence tomography (OCT), the subtle structural change caused by glaucoma can be detected earlier, even before the visual field loss would appear [5]
The age of the glaucoma groups was significantly older than the normal group (p < 0.001), and the best-corrected visual acuity (BCVA) of the glaucoma groups was significantly worse than the normal group (p < 0.001)
No significant difference was observed in the intraocular pressure (IOP) and spherical equivalent
Summary
Glaucoma is a potentially blinding disease, characterized by progressive degeneration of the retinal ganglion cells, resulting in distinct changes of the optic nerve head and corresponding visual field defect [1]. By observing the structural changes of the retinal nerve fiber layer, neuroretinal rim, or inner layer of macula, it is possible to detect the potential glaucoma patients [2]. Optical coherence tomography (OCT) is a non-invasive technology that enables highresolution cross-sectional images of ocular tissues and provides objective quantitative data that have good reproducibility [3,4]. Through the various scan protocols provided by OCT, the subtle structural change caused by glaucoma can be detected earlier, even before the visual field loss would appear [5]
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