Diagnostic Accuracy of Wide-Field Map from Swept-Source Optical Coherence Tomography for Primary Open-Angle Glaucoma in Myopic Eyes

Diagnostic ability of macular nerve fiber layer thickness measured by swept-source optical coherence tomography in preperimetric glaucoma

BACKGROUND

We evaluated the diagnostic ability of macula retinal nerve fiber layer (mRNFL) thickness in preperimetric glaucoma (PPG) patients.

METHODS

This prospective study included 83 patients with PPG and 83 age- and refractive error-matched normal control subjects. PPG was defined as a localized RNFL defect corresponding to glaucomatous optic disc changes with a normal visual field test. We used spectral-domain (SD) optical coherence tomography (OCT) to measure the circumpapillary RNFL (cpRNFL) thickness and macular ganglion cell-inner plexiform layer (GCIPL) thickness. Swept-source (SS) OCT was used to measure cpRNFL thickness, macular ganglion cell layer + inner plexiform layer (IPL) thickness (GCL+), and macular ganglion cell layer + IPL+ mRNFL thickness (GCL++). The mRNFL thickness was defined as GCL++ minus GCL+. To evaluate the diagnostic power of each parameter, the area under the receiver operating characteristics curve (AUROC) was analyzed to differentiate PPG from the normal groups.

RESULTS

Using SD-OCT, all GCIPL parameters and most cpRNFL parameters, except at the nasal and temporal quadrant, were significantly lower in PPG versus normal controls. PPG eyes had significantly smaller values than normal controls for all cpRNFL and GCL parameters measured by SS-OCT, except mRNFL at the superonasal area. The inferotemporal GCL++ had the largest AUROC value (0.904), followed by inferotemporal GCL+ (0.882), inferotemporal GCIPL thickness (0.871), inferior GCL++ (0.866), inferior cpRNFL thickness by SS-OCT (0.846), inferior cpRNFL thickness by SD-OCT (0.841), and inferotemporal mRNFL thickness (0.840). The diagnostic performance was comparable between inferotemporal mRNFL thickness and the best measures of GCL (inferotemporal GCL++, p = 0.098) and cpRNFL (inferior cpRNFL thickness by SS-OCT, p = 0.546).

CONCLUSION

The diagnostic ability of mRNFL thickness was comparable to that of the best measures of cpRNFL and GCL analysis for eyes with PPG. Therefore, mRNFL thickness could be a new parameter to detect early structural changes in PPG.

Wide-Field Optical Coherence Tomography Imaging Improves Rate of Change Detection in Progressing Glaucomatous Eyes Compared With Standard-Field Imaging

Purpose

To compare rates of retinal nerve fiber layer change over time in healthy, eyes with nonprogressing glaucoma and eyes with progressing glaucoma using single wide-field (SWF) and optic nerve head (ONH) cube scan optical coherence tomography (OCT) images.

Methods

Forty-five eyes of 25 healthy individuals and 263 eyes of 161 glaucoma patients from the Diagnostic Innovations in Glaucoma Study were included. All eyes underwent 24-2 visual field testing and OCT (Spectralis SD-OCT) ONH and macular imaging. SWF images (up to 43° × 28°) were created by stitching together ONH cube scans centered on the optic disc and macular cube scans centered on the fovea. Visual field progression was defined as guided progression analysis likely progression and/or a significant (P < 0.01) mean deviation slope of less than -1.0 dB/year. Mixed effects models were used to compare rates of change. Highly myopic eyes were included.

Results

Thirty glaucomatous eyes were classified as progressing. In eyes with glaucoma, mean global rate of change was -1.22 µm/year (P < 0.001) using SWF images and -0.83 µm/year (P = 0.003) using ONH cube scans. Rate of change was significantly greater in eyes with progressing glaucoma compared with eyes with nonprogressing glaucoma (-1.51 µm/year vs. -1.24 µm/year; P = 0.002) using SWF images and was similar using ONH cube scans (P = 0.27).

Conclusions

In this cohort that includes eyes with and without high axial myopia, the mean rate of retinal nerve fiber layer thinning measured using SWF images was faster in eyes with progressing glaucoma than in eyes with nonprogressing glaucoma. Wide-field OCT images including the ONH and macula can be effective for monitoring glaucomatous progression in patients with and without high myopia.

Diagnostic performance of wide-field optical coherence tomography angiography in detecting open-angle glaucoma in high myopia

PURPOSE

To compare the diagnostic performance of the capillary density (CD) of the central 1-6 mm and peripheral 6-12 mm annular regions in detecting open-angle glaucoma in high myopia (HM) using 15 × 12 mm wide-field swept-source optical coherence tomography angiography (WF SS-OCTA).

METHODS

The study enrolled 206 and 103 eyes with HM and highly myopic open-angle glaucoma (HM-OAG), respectively. WF SS-OCTA images centred on the fovea were obtained to analyse the changes in the CD in the 1-3 mm, 3-6 mm, 6-9 mm, and 9-12 mm annular regions. CD of the superficial capillary plexus (SCP) was measured with the built-in software. The area under the receiver operating characteristic curve (AUROC) of each region was compared.

RESULTS

The diagnostic performance of the SCP CD in the central 1-6 mm annular region (AUROC = 0.849) was better than that in the peripheral 6-12 mm annular region (AUROC = 0.756, p = 0.001). The annular AUROCs of SCP CD peaked in the 3-6 mm annular region (AUROC = 0.858) and gradually decreased with increasing diameter and were lower than the corresponding AUROCs of the ganglion cell-inner plexiform layer thickness (p < 0.05 for all comparisons). SCP CD of the inferior quadrant in the 3-6 mm annular region had the best diagnostic performance (AUROC = 0.859).

CONCLUSION

The SCP CD in the central 1-6 mm annular region exhibited better diagnostic performance for the detection of HM-OAG in HM. The assessment of more peripheral regions has no added value in detecting glaucoma in HM.

High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population

PURPOSE

To develop and validate the performance of a high myopia (HM)-specific normative database of peripapillary retinal nerve fiber layer (pRNFL) thickness in differentiating HM from highly myopic glaucoma (HMG).

DESIGN

Cross-sectional multicenter study.

PARTICIPANTS

A total of 1367 Chinese participants (2325 eyes) with nonpathologic HM or HMG were included from 4 centers. After quality control, 1108 eyes from 694 participants with HM were included in the normative database; 459 eyes from 408 participants (323 eyes with HM and 136 eyes with HMG) and 322 eyes from 197 participants (131 eyes with HM and 191 eyes with HMG) were included in the internal and external validation sets, respectively. Only HMG eyes with an intraocular pressure > 21 mmHg were included.

METHODS

The pRNFL thickness was measured with swept-source (SS) OCT. Four strategies of pRNFL-specified values were examined, including global and quadrantic pRNFL thickness below the lowest fifth or the lowest first percentile of the normative database.

MAIN OUTCOMES MEASURES

The accuracy, sensitivity, and specificity of the HM-specific normative database for detecting HMG.

RESULTS

Setting the fifth percentile of the global pRNFL thickness as the threshold, using the HM-specific normative database, we achieved an accuracy of 0.93 (95% confidence interval [CI], 0.90-0.95) and 0.85 (95% CI, 0.81-0.89), and, using the first percentile as the threshold, we acheived an accuracy of 0.85 (95% CI, 0.81-0.88) and 0.70 (95% CI, 0.65-0.75) in detecting HMG in the internal and external validation sets, respectively. The fifth percentile of the global pRNFL thickness achieved high sensitivities of 0.75 (95% CI, 0.67-0.82) and 0.75 (95% CI, 0.68-0.81) and specificities of 1.00 (95% CI, 0.99-1.00) and 1.00 (95% CI, 0.97-1.00) in the internal and external validation datasets, respectively. Compared with the built-in database of the OCT device, the HM-specific normative database showed a higher sensitivity and specificity than the corresponding pRNFL thickness below the fifth or first percentile (P < 0.001 for all).

CONCLUSIONS

The HM-specific normative database is more capable of detecting HMG eyes than the SS OCT built-in database, which may be an effective tool for differential diagnosis between HMG and HM.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Addressing Glaucoma in Myopic Eyes: Diagnostic and Surgical Challenges

Epidemiological and genetic studies provide strong evidence supporting an association between myopia and glaucoma. The accurate detection of glaucoma in myopic eyes, especially those with high myopia, remains clinically challenging due to characteristic morphologic features of the myopic optic nerve in addition to limitations of current optic nerve imaging modalities. Distinguishing glaucoma from myopia is further complicated by overlapping perimetric findings. Therefore, longitudinal follow-up is essential to differentiate progressive structural and functional abnormalities indicative of glaucoma from defects that may result from myopia alone. Highly myopic eyes are at increased risk of complications from traditional incisional glaucoma surgery and may benefit from newer microinvasive glaucoma surgeries in select cases.

Development of a deep learning system to detect glaucoma using macular vertical optical coherence tomography scans of myopic eyes

Myopia is one of the risk factors for glaucoma, making accurate diagnosis of glaucoma in myopic eyes particularly important. However, diagnosis of glaucoma in myopic eyes is challenging due to the frequent associations of distorted optic disc and distorted parapapillary and macular structures. Macular vertical scan has been suggested as a useful tool to detect glaucomatous retinal nerve fiber layer loss even in highly myopic eyes. The present study was performed to develop and validate a deep learning (DL) system to detect glaucoma in myopic eyes using macular vertical optical coherence tomography (OCT) scans and compare its diagnostic power with that of circumpapillary OCT scans. The study included a training set of 1416 eyes, a validation set of 471 eyes, a test set of 471 eyes, and an external test set of 249 eyes. The ability to diagnose glaucoma in eyes with large myopic parapapillary atrophy was greater with the vertical than the circumpapillary OCT scans, with areas under the receiver operating characteristic curves of 0.976 and 0.914, respectively. These findings suggest that DL artificial intelligence based on macular vertical scans may be a promising tool for diagnosis of glaucoma in myopic eyes.

Diagnostic ability of macular microvasculature with swept-source OCT angiography for highly myopic glaucoma using deep learning

Macular OCT angiography (OCTA) measurements have been reported to be useful for glaucoma diagnostics. However, research on highly myopic glaucoma is lacking, and the diagnostic value of macular OCTA measurements versus OCT parameters remains inconclusive. We aimed to evaluate the diagnostic ability of the macular microvasculature assessed with OCTA for highly myopic glaucoma and to compare it with that of macular thickness parameters, using deep learning (DL). A DL model was trained, validated and tested using 260 pairs of macular OCTA and OCT images from 260 eyes (203 eyes with highly myopic glaucoma, 57 eyes with healthy high myopia). The DL model achieved an AUC of 0.946 with the OCTA superficial capillary plexus (SCP) images, which was comparable to that with the OCT GCL+ (ganglion cell layer + inner plexiform layer; AUC, 0.982; P = 0.268) or OCT GCL++ (retinal nerve fiber layer + ganglion cell layer + inner plexiform layer) images (AUC, 0.997; P = 0.101), and significantly superior to that with the OCTA deep capillary plexus images (AUC, 0.779; P = 0.028). The DL model with macular OCTA SCP images demonstrated excellent and comparable diagnostic ability to that with macular OCT images in highly myopic glaucoma, which suggests macular OCTA microvasculature could serve as a potential biomarker for glaucoma diagnosis in high myopia.

Wide-field optical coherence tomography deviation map for early glaucoma detection

BACKGROUND/AIMS

This study aimed to establish a wide-field optical coherence tomography (OCT) deviation map obtained from swept-source OCT (SS-OCT) scans. Moreover, it also aimed to compare the diagnostic ability of this wide-field deviation map with that of the peripapillary and macular deviation maps currently being used for the detection of early glaucoma (EG).

METHODS

Four hundred eyes, including 200 healthy eyes and 200 eyes with EG were enrolled in this retrospective observational study. Patients underwent a comprehensive ocular examination, including wide-field SS-OCT (DRI-OCT Triton; Topcon, Tokyo, Japan). The individual wide-field scan was converted into a uniform template using the fovea and optic disc centres as fixed landmarks. Subsequently, the wide-field deviation map was obtained via the comparison between individual wide-field data and a normative wide-field database that had been created by combining images of healthy eyes into a uniform template in a previous study. The ability of the new wide-field deviation map to distinguish between EG and healthy eyes was assessed by comparing it with conventional deviation maps based on the area under the receiver operating characteristic curve (AUC).

RESULTS

The wide-field deviation map obtained using the normative wide-field database showed the highest diagnostic ability for the diagnosis of EG (AUC=0.980 and 961 for colour-coded pixels presenting <5% and <1%, respectively) among various deviation maps. Its AUC was significantly superior to that of most conventional deviation maps (p<0.05). The wide-field deviation map demonstrated early structural glaucomatous damage well over a wider area.

CONCLUSION

The wide-field SS-OCT deviation map exhibited good performance for distinguishing between eyes with EG and healthy eyes. The visualisation of the wider damaged area on the wide-field deviation map could be useful for the diagnosis of EG in clinical settings.

Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia

PURPOSE

To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell-inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps.

DESIGN

Comparison of diagnostic approaches.

METHODS

Cross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-μm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches.

RESULTS

The best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P ≤ .02 compared with texture for all comparisons).

CONCLUSION

The current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.

Widefield OCT Imaging for Quantifying Inner Retinal Thickness in the Nonhuman Primate

Purpose

To determine the agreement and repeatability of inner retinal thickness measures from widefield imaging compared to standard scans in healthy nonhuman primates.

Methods

Optical coherence tomography (OCT) scans were acquired from 30 healthy rhesus monkeys, with 11 animals scanned at multiple visits. The scan protocol included 20° × 20° raster scans centered on the macula and optic nerve head (ONH), a 12° diameter circular scan centered on the ONH, and a 55 × 45° widefield raster scan. Each scan was segmented using custom neural network–based algorithms. Bland–Altman analysis were used for comparing average circumpapillary retinal nerve fiber layer (RNFL) thickness and ganglion cell inner plexiform layer (GCIPL) thickness for a 16° diameter region. Comparisons were also made for similar 1° × 1° superpixels from the raster scans.

Results

Average circumpapillary RNFL thickness from the circular scan was 114.2 ± 5.8 µm, and 113.2 ± 7.3 µm for an interpolated scan path from widefield imaging (bias = −1.03 µm, 95% limits of agreement [LOA] −8.6 to 6.5 µm). GCIPL thickness from standard raster scans was 72.7 ± 4.3 µm, and 73.7 ± 3.7 µm from widefield images (bias = 1.0 µm, 95% LOA −2.4 to 4.4 µm). Repeatability for both RNFL and GCIPL standard analysis was less than 5.2 µm. For 1° × 1° superpixels, the 95% limits of agreement were between −13.9 µm and 13.7 µm for RNFL thickness and −2.5 µm and 2.5 µm for GCIPL thickness.

Conclusions

Inner retinal thickness measures from widefield imaging have good repeatability and are comparable to those measured using standard scans.

Translational Relevance

Monitoring retinal ganglion cell loss in the non-human primate experimental glaucoma model could be enhanced using widefield imaging.

A Myopic Normative Database for Retinal Nerve Fiber Layer Thickness using Optical Coherence Tomography

PRCIS

The purpose of this study was to determine changes in OCT color codes after applying a myopic normative database. The diagnostic performance of the retinal nerve fiber layer analysis improved with the use of this database.

PURPOSE

To evaluate the pRNFL color codes based on a newly generated myopic normative database in comparison to the built-in normative database.

METHODS

A total of 371 subjects were included in this validation study in an attempt to generate a myopic normative database. Eighty myopic glaucomatous and 80 myopic healthy eyes were evaluated to determine the diagnostic performance of this database. The distribution of the color codes was investigated among the groups with reference to the built-in and myopic normative databases, and the two databases were compared in terms of abnormal color code frequency. The diagnostic performance of the myopic database was presented with sensitivity, specificity and area under the receiver operating characteristics curve (AUROC) values.

RESULTS

The agreement between the databases decreased with increasing myopia degree. The distribution of the color codes of the built-in software significantly differed among the study groups in all sectors (P=0.009 for the temporal sector and P<0.001 for the remaining sectors). When the myopic database was used, there were no longer significant differences among the groups for the temporosuperior, temporoinferior, temporal and nasal sectors (P=0.561, 0.299, 0.201, and 0.089 respectively). After applying the myopic normative database, the specificity of the pRNFL color codes increased from 70.1% to 90.2%, and the AUROC value from 0.851 to 0.945.

CONCLUSIONS

The use of a myopic normative database for pRNFL using SD-OCT significantly decreased differences among myopia severity groups, and may help to more reliably assess glaucoma in myopic eyes.

Comparison of Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness Values Using Spectral-Domain and Swept-Source OCT

Purpose

To compare peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) thickness measurements obtained with spectral domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) using an OCT-angiography scanning protocol, and their ability to distinguish among patients with glaucoma, glaucoma suspects (GS), and healthy controls (HC).

Methods

Cross-sectional study of 196 eyes (81 glaucoma, 48 GS, and 67 HC) of 119 participants. Participants underwent peripapillary and macular OCT with SD-OCT and SS-OCT. Parameters of interest were average and sector-wise pRNFL and mGCIPL thickness. Inter-device agreement was investigated with Bland-Altman statistics. Conversion formulas were developed with linear regression. Diagnostic performances were evaluated with area under the receiver operating characteristic curves.

Results

Both SD-OCT and SS-OCT detected a significant pRNFL and mGCIPL thinning in glaucoma patients compared to HC and GS for almost all study sectors. A strong linear relationship between the two devices was present for all quadrants/sectors (R2 ≥ 0.81, P < 0.001), except for the nasal (R2 = 0.49, P < 0.001) and temporal (R2 = 0.62, P < 0.001) pRNFL quadrants. SD-OCT and SS-OCT measurements had a proportional bias, which could be removed with conversion formulas. Overall, the two devices showed similar diagnostic abilities.

Conclusions

Thickness values obtained with SD-OCT and SS-OCT are not directly interchangeable but potentially interconvertible. Both devices have a similar ability to discriminate glaucoma patients from GS and healthy subjects.

Translational Relevance

OCT-Angiography scans can be reliably used to obtain structural metrics in glaucoma patients.

Combined wide-field optical coherence tomography angiography density map for high myopic glaucoma detection

The present study aimed to evaluate the diagnostic ability of wide-field optical coherence tomography angiography (OCTA) density map for detection of glaucomatous damage in high myopic (HM) eyes and to further compare the diagnostic ability of OCTA with that of conventional imaging approaches including red-free photography and swept-source OCT (SS-OCT) wide-field maps. A total of 77 healthy HM eyes and 72 HM eyes with open angle glaucoma (OAG) participated in this retrospective observational study. Patients underwent a comprehensive ocular examination, including wide-field SS-OCT scan and peripapillary area and macular OCTA scans. An integrated OCTA density map thereafter was merged by vascular landmark-guided superimposition of peripapillary and macular superficial vascular density maps onto the red-free photography (resulting in the OCTA-PanoMap). Glaucoma specialists then determined the presence of glaucomatous damage in HM eyes by reading the OCTA-PanoMap and compared its sensitivity and specificity with those of conventional images. Sensitivity and specificity of OCTA-PanoMap for HM-OAG diagnosis was 94.4% and 96.1%, respectively. Compared with other imaging methods, the sensitivity of OCTA-PanoMap was significantly higher than that of red-free photography (P = 0.022) and comparable to that of wide-field SS-OCT maps. Specificity of OCTA-PanoMap was significantly higher than those of other conventional imaging methods (except for wide-field thickness map). The OCTA-PanoMap showed good diagnostic ability for discrimination of HM-OAG eyes from healthy HM eyes. As a complementary method of an alternative imaging modality, OCTA-PanoMap can be a useful tool for detection of HM-OAG.

Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images

PURPOSE

(1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing glaucoma using a variety of wide-field SS-OCT images and compare their diagnostic abilities with that of conventional parameter-based methods.

METHODS

Overall, 675 eyes, including 258 healthy eyes and 417 eyes with glaucoma were enrolled in this retrospective observational study. Each single-page wide-field report (12×9 mm) of wide-field SS-OCT imaging provides different types of images that reflect the state of the eyes. A DL-based automated diagnosis system was proposed to detect glaucoma and identify its stage based on such images. We applied the convolutional neural network to each type of image to detect glaucoma. In addition, 2 fusion strategies, fusion by convolution network (FCN) and fusion by fully connected network (FFC) were developed; they differ in terms of the level of fusion of features derived from convolutional neural networks. The diagnostic models were trained using 382 and 293 images in the training and test data sets, respectively. The diagnostic ability of this method was compared with conventional parameters of the thickness of the retinal nerve fiber layer and ganglion cell complex.

RESULTS

FCN achieved an area under the receiver operating characteristic curve (AUC) of 0.987 (95% confidence interval, CI: 0.968-0.996) and an accuracy of 95.22%. In contrast, FFC achieved an AUC of 0.987 (95% CI, 0.971-0.998) and an accuracy of 95.90%. Both FCN and FFC outperformed the conventional method (P<0.001). In detecting early glaucoma, both FCN and FFC achieved significantly higher AUC and accuracy than the conventional approach (P<0.001). In addition, the classification performance of the DL-based fusion methods in identifying the 5 stages of glaucoma is presented via a confusion matrix.

CONCLUSION

DL protocol based on wide-field OCT images outperformed the conventional method in terms of both AUC and accuracy. Therefore, DL-based diagnostic methods using wide-field OCT images are promising in diagnosing glaucoma in clinical practice.