A Single Wide-Field OCT Protocol Can Provide Compelling Information for the Diagnosis of Early Glaucoma

Age-related changes in the peripheral retinal nerve fiber layer thickness

Purpose

This pilot cross-sectional study aimed to determine age-related changes of the retinal nerve fiber layer (RNFL) thickness in retinal periphery by swept-source optical coherence tomography-based analysis.

Methods

Forty eyes of 40 healthy subjects were studied in three age groups, group 1 (20–40 years, n=15), group 2 (41–60 years, n=14), and group 3 (≥61 years, n=11). Wide-angle swept-source optical coherence tomography scans, including the optic disc and macula, were montaged with the nasal peripheral optical coherence tomography images acquired with a contralateral gaze. The peripapillary and peripheral RNFL thickness values were obtained for nasal and temporal sides. The ratio of peripheral-to-peripapillary RNFL thickness was also calculated for these sectors.

Results

We detected a significantly thinner RNFL in older than younger subjects at a distance of 6 mm from the optic disc on nasal and temporal sides (P<0.001). An age-related reduction in peripheral-to-peripapillary RNFL thickness ratios (P<0.001 and P<0.02 for nasal and temporal sides, respectively) was also detected.

Conclusion

The age-related decline should be taken into consideration when determining the glaucoma-related alterations in peripheral RNFL thickness. Continued analysis in patients with ocular hypertension and glaucoma should help determine whether RNFL in the periphery with lower nerve tissue reserve might be more susceptible to injury, whether injury to the peripheral RNFL might be easier to detect, and/or whether analysis of the peripheral RNFL thickness could improve clinical diagnosis and follow-up of glaucoma.

Nerve Fiber Flux Analysis Using Wide-Field Swept-Source Optical Coherence Tomography

Purpose

To devise a method to quantify nerve fibers over their arcuate courses over an extended peripapillary area using optical coherence tomography (OCT).

Methods

Participants were imaged with 8 × 8-mm volumetric OCT scans centered at the optic disc. A new quantity, nerve fiber flux (NFF), represents the cross-sectional area transected perpendicular to the nerve fibers. The peripapillary area was divided into 64 tracks with equal flux. An iterative algorithm traced the trajectory of the tracks assuming that the relative distribution of the NFF was conserved with compensation for fiber connections to ganglion cells on the macular side. Average trajectory was averaged from normal eyes and use to calculate the NFF maps for glaucomatous eyes. The NFF maps were divided into eight sectors that correspond to visual field regions.

Results

There were 24 healthy and 10 glaucomatous eyes enrolled. The algorithm converged on similar patterns of NFL tracks for all healthy eyes. In glaucomatous eyes, NFF correlated with visual field sensitivity in the arcuate sectors (Spearman ρ = 0.53–0.62). Focal nerve fiber loss in glaucomatous eyes appeared as uniform tracks of NFF defects that followed the expected arcuate fiber trajectory.

Conclusions

Using an algorithm based on the conservation of flux, we derived nerve fiber trajectories in the peripapillary area. The NFF map is useful for the visualization of focal defects and quantification of sector nerve fiber loss from wide-area volumetric OCT scans.

Translational Relevance

NFF provides a cumulative measure of volumetric loss along nerve fiber tracks and could improve the detection of focal glaucoma damage.

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects

PURPOSE

Existing summary statistics based upon optical coherence tomographic (OCT) scans and/or visual fields (VFs) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hybrid deep learning method (HDLM), combined with a single wide-field OCT protocol, can distinguish eyes previously classified as either healthy suspects or mild glaucoma.

METHODS

In total, 102 eyes from 102 patients, with or suspected open-angle glaucoma, had previously been classified by 2 glaucoma experts as either glaucomatous (57 eyes) or healthy/suspects (45 eyes). The HDLM had access only to information from a single, wide-field (9×12 mm) swept-source OCT scan per patient. Convolutional neural networks were used to extract rich features from maps derived from these scans. Random forest classifier was used to train a model based on these features to predict the existence of glaucomatous damage. The algorithm was compared against traditional OCT and VF metrics.

RESULTS

The accuracy of the HDLM ranged from 63.7% to 93.1% depending upon the input map. The retinal nerve fiber layer probability map had the best accuracy (93.1%), with 4 false positives, and 3 false negatives. In comparison, the accuracy of the OCT and 24-2 and 10-2 VF metrics ranged from 66.7% to 87.3%. The OCT quadrants analysis had the best accuracy (87.3%) of the metrics, with 4 false positives and 9 false negatives.

CONCLUSIONS

The HDLM protocol outperforms standard OCT and VF clinical metrics in distinguishing healthy suspect eyes from eyes with early glaucoma. It should be possible to further improve this algorithm and with improvement it might be useful for screening.

Diagnostic Ability of Wide-field Retinal Nerve Fiber Layer Maps Using Swept-Source Optical Coherence Tomography for Detection of Preperimetric and Early Perimetric Glaucoma

PURPOSE

To evaluate the diagnostic ability of wide-field retinal nerve fiber layer (RNFL) maps with swept-source optical coherence tomography (SS-OCT) for detection of preperimetric (PPG) and early perimetric glaucoma (EG).

METHODS

One hundred eighty-four eyes, including 67 healthy eyes, 43 eyes with PPG, and 74 eyes with EG, were analyzed. Patients underwent a comprehensive ocular examination including red-free RNFL photography, visual field testing and wide-field SS-OCT scanning (DRI-OCT-1 Atlantis; Topcon, Tokyo, Japan). SS-OCT provides a wide-field RNFL thickness map and a SuperPixel map, which are composed of the RNFL deviation map of the peripapillary area and the deviation map of the composition of the ganglion cell layer with the inner plexiform layer and RNFL (GC-IPL+RNFL) in the macular area. The ability to discriminate PPG and EG from healthy eyes was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for all parameters and criteria provided by the wide-field SS-OCT scan.

RESULTS

The wide-field RNFL thickness map using SS-OCT showed the highest sensitivity of PPG-diagnostic and EG-diagnostic performance compared with the other SS-OCT criteria based on the internal normative base (93.0 and 97.3%, respectively). Among the SS-OCT continuous parameters, the RFNL thickness of the 7 clock-hour, inferior and inferotemporal macular ganglion cell analyses showed the largest AUC of PPG-diagnostic and EG-diagnostic performance (AUC=0.809 to 0.865).

CONCLUSIONS

The wide-field RNFL thickness map using SS-OCT performed well in distinguishing eyes with PPG and EG from healthy eyes. In the clinical setting, wide-field RNFL maps of SS-OCT can be useful tools for detection of early-stage glaucoma.

Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma

Purpose

A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects.

Methods

Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study.

Results

We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB).

Conclusions

The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.

Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT)

Although ophthalmologists are becoming increasingly reliant upon optical coherence tomography (OCT), clinicians who care for glaucoma patients are not taking full advantage of the potential of this powerful technology. First, we ask, how would one describe the nature of glaucomatous damage if only OCT scans were available? In particular, a schematic model of glaucomatous damage is developed in section 2, and the nature of glaucomatous damage seen on OCT scans described in the context of this model in section 3. In particular, we illustrate that local thinning of the circumpapillary retinal nerve fiber layer (cpRNFL) around the optic disc can vary in location, depth, and/or width, as well as homogeneity of damage. Second, we seek to better understand the relationship between the thinning of the cpRNFL and the various patterns of sensitivity loss seen on visual fields obtained with standard automated perimetry. In sections 4 and 5, we illustrate why one should expect a wide range of visual field patterns, and iilustrate why they should not be placed into discrete categories. Finally, section 6 describes how the clinician can take better advantage of the information in OCT scans. The approach is summarized in a single-page report, which can be generated from a single wide-field scan. The superiority of this approach, as opposed to the typical reliance on summary metrics, is described.