Quantitative Evaluation of Gonioscopic and EyeCam Assessments of Angle Dimensions Using Anterior Segment Optical Coherence Tomography

Xu, Benjamin; Pardeshi, Anmol A.; Burkemper, Bruce; Richter, Grace M.; Lin, Shan; McKean‐Cowdin, Roberta; Varma, Rohit

doi:10.1167/tvst.7.6.33

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…Similarly, the scleral spur (grade 3) was also indistinguishable from both posterior trabecular meshwork (grade 2) and ciliary body (grade 4). Similar to the work of Xu et al, 15 we also found a wider distribution of quantitative values for grades 3 and 4, supporting the notion that the measurement ceiling is not well-defined. This was demonstrated in both evaluation of the distributions of quantitative values and the results of the clustering algorithm identifying a distinct subset of angles with very large quantitative values.…”

Section: Inequalities Contribute To Discordance Between Gonioscopic Gsupporting

confidence: 91%

Assessment of angle closure spectrum disease as a continuum of change using gonioscopy and anterior segment optical coherence tomography

Phu

Wong

Lim

et al. 2020

Ophthalmic Physiologic Optic

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Purpose Studies examining the anterior chamber angle and angle closure disease often compare quantitative angle information obtained using anterior segment optical coherence tomography (ASOCT) with one of several ordinal scales derived using gonioscopy. We test the assumption that the ordinal gonioscopic angle grades have equal step sizes and can be analysed using metric statistics. Methods The medical records of 214 consecutive patients who were referred for assessment of the anterior chamber angle were prospectively examined using gonioscopy and ASOCT (Spectralis Optical Coherence Tomography, OCT, http://www.heidelbergengineering.com). Anterior chamber angle parameters (angle opening distance, AOD, and trabecular‐iris space area, TISA at 500 and 750 microns) were extracted from ASOCT images using a semi‐automated segmentation algorithm written on MATLAB (http://www.mathworks.com). We first matched the quantitative values for each gonioscopic grade (0–4, from no structures visible to ciliary body visible) and described the frequency distributions to determine separability. We then applied a grade‐agnostic clustering algorithm to determine the concordance between algorithm‐clustered groups (using solely quantitative data) and those obtained using gonioscopy. Results The frequency distributions of the quantitative ASOCT parameters for each angle grade were mostly non‐parametric and displayed unique distribution characteristics, with a floor effect seen for grade 0 and the lack of a ceiling effect seen for grades 3 and 4. Although we found significant differences in quantitative values across the five angle grades using the frequency distributions, some pairwise comparisons were indistinguishable (such as grades 0 and 1, and grades 3 and 4) due to the overlaps in distributions. On average, differences in quantitative values were consistent between gonioscopic grade steps, but there remained substantial variability that confounds prediction of change between ordinal steps. The clustering algorithm showed approximately 10% of cases with the same group assignment as that of the gonioscopic grade, improving slightly to 30% when the top 5% of quantitative data were excluded from analysis. Conclusions Our results do not necessarily support the assumption that the ordinal scales used in gonioscopy can be interpreted using an interval scale. We highlight the need for better methods of describing the course and risk of angle closure spectrum disease to identify disease progression and conversion, where gonioscopy remains the gold standard.

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Section: Inequalities Contribute To Discordance Between Gonioscopic Gsupporting

confidence: 91%

Assessment of angle closure spectrum disease as a continuum of change using gonioscopy and anterior segment optical coherence tomography

Phu

Wong

Lim

et al. 2020

Ophthalmic Physiologic Optic

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“…30 This finding may be related to the fact gonioscopy grades are only weakly associated with angle width in eyes with angle closure. 31 Conversely, quantitative AS-OCT measurements of angle width have a strong association with intraocular pressure, a strong risk factor for glaucomatous optic neuropathy, in a subset of eyes with primary angle closure disease. 32 However, the clinical utility of this finding is currently limited by the technical challenges associated with manual analysis of AS-OCT images.…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Network for Scleral Spur Detection in Anterior Segment OCT Images: The Chinese American Eye Study

Chiang

Pardeshi

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose To develop a deep neural network that detects the scleral spur in anterior segment optical coherence tomography (AS-OCT) images. Methods Participants in the Chinese American Eye Study, a population-based study in Los Angeles, California, underwent complete ocular examinations, including AS-OCT imaging with the Tomey CASIA SS-1000. One human expert grader provided reference labels of scleral spur locations in all images. A convolutional neural network (CNN)-based on the ResNet-18 architecture was developed to detect the scleral spur in each image. Performance of the CNN model was assessed by calculating prediction errors, defined as the difference between the Cartesian coordinates of reference and CNN-predicted scleral spur locations. Prediction errors were compared with intragrader variability in detecting scleral spur locations by the reference grader. Results The CNN was developed using a training dataset of 17,704 images and tested using an independent dataset of 921 images. The mean absolute prediction errors of the CNN model were 49.27 ± 42.07 µm for X-coordinates and 47.73 ± 39.70 µm for Y-coordinates. The mean absolute intragrader variability was 52.31 ± 47.75 µm for X-coordinates and 45.88 ± 45.06 µm for Y-coordinates. Distributions of prediction errors for the CNN and intragrader variability for the reference grader were similar for X-coordinates ( P = 0.609) and Y-coordinates ( P = 0.378). The mean absolute prediction error of the CNN was 73.08 ± 52.06 µm and the mean absolute intragrader variability was 73.92 ± 60.72 µm. Conclusions A deep neural network can detect the scleral spur on AS-OCT images with performance similar to that of a human expert grader. Translational Relevance Deep learning methods that automate scleral spur detection can facilitate qualitative and quantitative assessments of AS-OCT images.

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“…Gonioscopy, performed in a dark room (with and without indentation), was performed on all participants by a single examiner (ZHY), who was masked to the UBM findings, using a Goldmann two-mirror lens (Ocular Instruments Inc., Bellevue, WA, USA). The angle in each quadrant was graded based on the observed anatomical structures, following the modified Shaffer grading system [ 8 , 9 ] (grade 0, no structure or Schwalbe's line observed; grade 1, visible anterior nonpigmented trabecular meshwork; grade 2, visible posterior pigmented trabecular meshwork; grade 3, visible scleral spur; grade 4, visible ciliary body). Angle quadrants were considered closed if they were grade 0 or 1.…”

Section: Methodsmentioning

confidence: 99%

Comparisons of Ocular Anatomic Differences of Lens-Subluxated Eye with or without Acute Angle Closure: A Retrospective Study

Lin

Qin

Zhang

et al. 2020

Journal of Ophthalmology

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Purpose. To compare ocular anatomy differences of lens subluxation between eyes with or without acute angle closure (AAC). Methods. This is a retrospective and case-control study. Sixty cases with mild lens subluxation were recruited. Among them, 30 eyes with acute angle closure were assigned to the AAC group and 30 eyes without AAC were assigned to the non-AAC group. The anterior segment was quantitatively evaluated by ultrasound biomicroscopy (UBM). The axial length (AL) was measured with IOL Master. All patients underwent lens extraction surgery and were followed up for six months. Results. The history of blunt trauma accounted for 22 (73.3%) cases in the AAC group and 21 (70%) cases in the non-AAC group. Fifteen (50%) patients in the AAC group had iridotomy history, and high intraocular pressure recurred. The UBM analysis showed that the average central chamber depth of the affected eyes in the AAC group was 1.82 mm, which was significantly shallower than that in the fellow eyes (2.58 mm, P<0.05) or both eyes in the non-AAC group.Both eyes in the AAC group presented a shorter AL and shallower anterior chamber than the eyes in the non-AAC group. Conclusions. An asymmetrical anterior chamber between bilateral eyes is an important feature in lens subluxation-induced AAC. The crowded anterior chamber and shorter AL might be the anatomic basis for the eye with lens subluxation-induced AAC.

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Quantitative Evaluation of Gonioscopic and EyeCam Assessments of Angle Dimensions Using Anterior Segment Optical Coherence Tomography

Cited by 22 publications

References 28 publications

Assessment of angle closure spectrum disease as a continuum of change using gonioscopy and anterior segment optical coherence tomography

Assessment of angle closure spectrum disease as a continuum of change using gonioscopy and anterior segment optical coherence tomography

Deep Neural Network for Scleral Spur Detection in Anterior Segment OCT Images: The Chinese American Eye Study

Comparisons of Ocular Anatomic Differences of Lens-Subluxated Eye with or without Acute Angle Closure: A Retrospective Study

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