Stereo Photo Measured ONH Shape Predicts Development of POAG in Subjects With Ocular Hypertension

SD-OCT peripapillary nerve fibre layer and ganglion cell complex parameters in glaucoma: principal component analysis

BACKGROUND/AIMS

To identify objective glaucoma-related structural features based on peripapillary (p) and macular (m) spectral domain optical coherence tomography (SD-OCT) parameters and assess their discriminative ability between healthy and glaucoma patients.

METHODS

Two hundred and sixty eyes (91 controls and 169 glaucoma) were included in this prospective study. After a complete examination, all participants underwent the posterior pole and the peripapillary retinal nerve fibre layer (pRNFL) protocols of the Spectralis SD-OCT. Principal component analysis (PCA), a data reduction method, was applied to identify and characterise the main information provided by the ganglion cell complex (GCC). The discriminative ability between healthy and glaucomatous eyes of the first principal components (PCs) was compared with that of conventional SD-OCT parameters (pRNFL, macular RNFL (mRNFL), macular ganglion cell layer (mGCL)and macular inner plexiform layer (mIPL)) using 10-fold cross-validated areas under the curve (AUC).

RESULTS

The first PC explained 58% of the total information contained in the GCC and the pRNFL parameters and was the result of a general combination of almost all variables studied (diffuse distribution). Other PCs were driven mainly by pRNFL and mRNFL measurements. PCs and pRNFL had similar AUC (0.95 vs 0.96, p=0.88), and outperformed the other structural measurements: mRNFL (0.91, p=0.002), mGCL (0.92, p=0.02) and mIPL (0.92, p=0.0001).

CONCLUSIONS

PCA identified a diffuse representation of the papillary and macular SD-OCT parameters as the most important PC to summarise structural data in healthy and glaucomatous eyes. PCs and pRNFL parameters showed the greatest discriminative ability between healthy and glaucoma cases.

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression

Purpose

To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression.

Methods

Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures.

Results

The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R² = 0.49, P < 0.0001) and FDT visual field testing (R² = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R² = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively).

Conclusions

A computational approach can identify structural features that improve glaucoma detection and progression prediction.