Assessment of Iris Trabecular Contact in Eyes with Gonioscopic Angle-Closure.

PURPOSE: To investigate the extent of iris trabecular contact (ITC) measured by anterior segment OCT (AS-OCT) and its association with primary angle-closure (PAC) and PAC glaucoma (PACG) in eyes with gonioscopic angle-closure and to determine the diagnostic performance of ITC for detection of gonioscopic angle-closure. DESIGN: Multicenter, prospective study. PARTICIPANTS: A total of 119 healthy participants with gonioscopic open-angle and 170 patients with gonioscopic angle-closure (94 with PAC suspect and 76 with PAC/PACG) were included. METHODS: One eye of each subject was randomly selected for AS-OCT imaging. Angle-opening distance (AOD500) and trabecular iris space area (TISA500) were measured every 10° for 360°. Two criteria of ITC500 were examined: (1) AOD500 = 0 mm and (2) TISA500 = 0 mm2. The association between the extent of ITC500 and PAC/PACG in eyes with gonioscopic angle-closure was analyzed with logistic regression analysis. MAIN OUTCOME MEASURES: Sensitivity and specificity of ITC500 for detection of gonioscopic angle-closure; odds ratio (OR) of PAC/PACG. RESULTS: The sensitivity of ITC500 ≥ 10° for detection of gonioscopic angle-closure ranged from 82.4% (AOD500 = 0 mm) to 84.7% (TISA500 = 0 mm2), and the specificity was 85.7% (for both AOD500 = 0 mm and TISA500 = 0 mm2). The extent of ITC500 determined by AS-OCT, not cumulative gonioscopy score (i.e., the sum of the modified Shaffer grades over 4 quadrants), was associated with the odds of PAC/PACG in eyes with gonioscopic angle-closure; the odds of PAC/PACG increased by 5% for every 10° increase in ITC500 (OR, 1.051, 95% confidence interval [CI], 1.022-1.080 for AOD500 = 0 mm; OR, 1.049, 95% CI, 1.022-1.078 for TISA500 = 0 mm2). Axial length and anterior chamber depth were not associated with PAC/PACG in eyes with gonioscopic angle-closure (P ≥ 0.574). CONCLUSIONS: A greater extent of ITC measured by AS-OCT, not angle-closure determined by gonioscopy, was associated with a greater odds of PAC/PACG in eyes with gonioscopic angle-closure.

Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT.

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

Angle-closure assessment in anterior segment OCT images via deep learning.

Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients' eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy.