Assessment of angle closure disease in the age of artificial intelligence: A review.

Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS: The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION: The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

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.

Recent Trends in Treatment and Associated Costs of Primary Angle-Closure Glaucoma: A Retrospective Cohort Study.

PURPOSE: To describe recent trends in the treatment for primary angle-closure glaucoma (PACG) and its associated costs in a clinical setting. DESIGN: A retrospective cohort study. SUBJECTS: We included patients with PACG from 2011 to 2020 using a large-scale administrative claims database in Japan. METHODS: We calculated the frequencies and costs of antiglaucoma drugs, ophthalmic examinations, and glaucoma-related surgeries, stratified by fiscal years and age groups. MAIN OUTCOME MEASURES: Frequencies and costs of antiglaucoma drugs, ophthalmic examinations, and glaucoma-related surgeries. RESULTS: We identified 5654 patients with PACG (15 338 patient-years). Prostanoid FP receptor agonist, nonselective ß-blocker, and topical carbonic anhydrase inhibitor use decreased, whereas prostanoid EP2 receptor agonist, α-2 adrenergic agonist, Rho-associated protein kinase inhibitor, and fixed-combination eyedrops use increased. The total amount of drug per patient-year significantly decreased. In recent years, the frequency of cataract surgery increased, whereas that of laser peripheral iridotomy decreased. Visual field testing, slit-lamp examination, intraocular pressure measurement, and funduscopy were performed 0.83, 6.65, 5.15, and 4.61 times/patient-year, respectively. The total cost of drugs, examinations, and surgeries was 60 338 yen per patient-year. Patients with PACG spent more than twice the money on surgeries and examinations than they did on antiglaucoma drugs. CONCLUSION: The amount of antiglaucoma drugs dispensed decreased, and the proportion of fixed-combination and newly introduced eyedrops increased. Frequency of cataract surgery increased whereas that of laser peripheral iridotomy decreased in recent years. Surgeries and examinations were the major cost drivers for PACG treatment. The current results would be valuable information for future economic analyses and policy making. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Ultrasound biomicroscopy in glaucoma assessment.

Ultrasound biomicroscopy (UBM) is an important tool in the diagnosis, evaluation and follow up of glaucoma patients. Even if we are dealing with a primary angle closure glaucoma (PACG) or a primary open angle glaucoma (POAG) patient, the mechanism of angle closure can be revealed by performing an UBM. The device can help differentiate between the two types of glaucoma even in patients with opaque corneas when gonioscopy cannot be performed. Knowing the type of glaucoma is vital, especially regarding an individualized treatment, since each patient is unique and needs to be treated accordingly, in order to prevent glaucomatous optic neuropathy and visual field loss. Abbreviations: AC = anterior chamber, ICE = iridocorneal endothelial syndrome, IOP = intraocular pressure, NTG = normal tension glaucoma, PACG = primary angle closure glaucoma, PC = posterior chamber, PEX = pseudoexfoliation syndrome, POAG = primary open angle glaucoma, UBM = ultrasound biomicroscopy.