Using Enhanced Depth Imaging Optical Coherence Tomography-Derived Parameters to Discriminate between Eyes with and without Glaucoma: A Cross-Sectional Comparative Study

Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.

Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis

PURPOSE

To compare the performance of 2 relatively recent geometric deep learning techniques in diagnosing glaucoma from a single optical coherence tomographic (OCT) scan of the optic nerve head (ONH); and to identify the 3-dimensional (3D) structural features of the ONH that are critical for the diagnosis of glaucoma.

DESIGN

Comparison and evaluation of deep learning diagnostic algorithms.

METHODS

In this study, we included a total of 2247 nonglaucoma and 2259 glaucoma scans from 1725 participants. All participants had their ONHs imaged in 3D with Spectralis OCT. All OCT scans were automatically segmented using deep learning to identify major neural and connective tissues. Each ONH was then represented as a 3D point cloud. We used PointNet and dynamic graph convolutional neural network (DGCNN) to diagnose glaucoma from such 3D ONH point clouds and to identify the critical 3D structural features of the ONH for glaucoma diagnosis.

RESULTS

Both the DGCNN (area under the curve [AUC]: 0.97±0.01) and PointNet (AUC: 0.95±0.02) were able to accurately detect glaucoma from 3D ONH point clouds. The critical points (ie, critical structural features of the ONH) formed an hourglass pattern, with most of them located within the neuroretinal rim in the inferior and superior quadrant of the ONH.

CONCLUSIONS

The diagnostic accuracy of both geometric deep learning approaches was excellent. Moreover, we were able to identify the critical 3D structural features of the ONH for glaucoma diagnosis that tremendously improved the transparency and interpretability of our method. Consequently, our approach may have strong potential to be used in clinical applications for the diagnosis and prognosis of a wide range of ophthalmic disorders.

Diagnostic Performance of Optical Coherence Tomography for Pseudoexfoliation Glaucoma

PRECIS

LC thickness and LCCI had comparable diagnostic performances with RNFL thickness in distinguishing eyes with PXG from those with PXS. BMO-MRW showed the lowest diagnostic performance among all geometric parameters derived from OCT scans we evaluated.

OBJECTIVE

To compare the diagnostic performance of different geometric parameters derived from optical coherence tomography (OCT) scans (retinal nerve fiber layer [RNFL] thickness, lamina cribrosa [LC] thickness, LC curvature index [LCCI] and Bruch's membrane opening-minimum rim width [BMO-MRW]) for distinguishing eyes with pseudoexfoliation glaucoma (PXG) from pseudoexfoliation syndrome (PXS) and healthy eyes.

METHODS

Fifty-five eyes of 55 patients with PXG, 55 eyes of 55 patients with PXS, and 50 healthy subjects were enrolled in this cross-sectional study. The areas under the receiver operating characteristic curves (AUCs) of RNFL thickness, LC thickness, LCCI and BMO-MRW were calculated and compared.

RESULTS

In discriminating between eyes with PXG from those with PXS, LC thickness (0.930 [95% CI: 0.883-0.978]) and global RNFL thickness (0.974 [95% CI: 0.947-0.992]) presented comparable AUCs (P=0.244). In distinguishing subjects wiht PXG from healthy controls, both LC thickness (0.972 [95% CI: 0.948-0.997]) and LCCI (0.983 [95% CI: 0.968-0.998]) had comparable AUCs with global RNFL thickness (0.988 [95% CI: 0.974-1.000]) (P=0.094 andP=0.239, respectively). Global BMO-MRW had lower AUCs than RNFL thickness (0.839 [95% CI: 0.759-0.920] and 0.897 [95% CI: 0.836-0.958], respectively) in distinguishing PXG from both PXS and healthy controls (P=0.001 andP=0.002, respectively). BMO-MRW also had significantly lower AUCs than both LC thickness and LCCI in distinguishing PXG from healthy controls (P=0.034 andP=0.001, respectively).

CONCLUSION

LC thickness and LCCI had better diagnostic performance than BMO-MRW in distinguishing PXG from PXS and healthy controls, which were comparable to RNFL thickness.