ArticleOphthalmology. Glaucoma
Differences in Ocular Biometric Measurements among Subtypes of Primary Angle Closure Disease: The Chinese American Eye Study.
Article in Ophthalmology. Glaucoma. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Ocular Biometric Risk Factors for Progression of Primary Angle Closure Disease: The Zhongshan Angle Closure Prevention Trial.Ophthalmology · 2022Trial
- Quantitative Assessment of Anterior Segment OCT Parameters and Their Association with Intraocular Pressure in Primary Angle-Closure Disease.Diagnostics (Basel, Switzerland) · 2026Article
- Differences in Screening Indicator Performance for Primary Angle Closure and Primary Angle Closure Diseases: The Handan Eye Study.Ophthalmology science · 2026Article
- Patterns and Disparities in Recorded Gonioscopy During Initial Glaucoma Evaluations in the United States.American journal of ophthalmology · 2024Article
- The Application of MicroRNAs in Glaucoma Research: A Bibliometric and Visualized Analysis.International journal of molecular sciences · 2023Article
- Classification of Angle Closure Severity by Hierarchical Cluster Analysis of Ocular Biometrics in the Dark and Light.Translational vision science & technology · 2023Article
- Ocular Biometric Determinants of Dark-to-Light Change in Angle Width: The Chinese American Eye Study.American journal of ophthalmology · 2022Article
- Applications of deep learning in the analysis of optical coherence tomography images for glaucoma-related diagnostics.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo assess differences in ocular biometric measurements between primary angle closure suspect (PACS) eyes and primary angle closure (PAC) and primary angle closure glaucoma (PACG) eyes.
designCross-sectional study.
participantsPatients with primary angle closure disease (PACD) were identified from the Chinese American Eye Study, a population-based study in Los Angeles, California.
methodsPatients previously underwent complete ocular examinations including gonioscopy and anterior segment (AS)-OCT imaging with the Tomey CASIA SS-1000 (Tomey Corporation). Four AS-OCT images were analyzed per eye. Averaged and sectoral measurements of biometric parameters, including angle recess area (ARA), trabecular iris space area (TISA), iris area, iris curvature, lens vault, anterior chamber depth, and anterior chamber area, were compared between early PACD (PACS) and late PACD (PAC and PACG) groups. Machine learning classifiers that attempt to differentiate between early and late PACD eyes were developed by applying different regression algorithms to a training dataset of sectoral parameter measurements. Classifier performance was assessed using an independent test dataset.
main outcome measuresAveraged and sectoral measurements of biometric parameters.
resultsTwo hundred ninety-eight eyes (231 PACS, 67 PAC or PACG) of 298 patients were analyzed. No difference was found in averaged biometric measurements between the 2 groups before (P > 0.09) or after (P > 0.14) adjusting for age and gender. Differences (P < 0.04) between the 2 groups were found for 11 sectoral parameter measurements, including ARA and TISA. The performance of machine learning classifiers developed using sectoral parameter measurements was poor on the independent test dataset for all regression algorithms (area under the receiver operating characteristic curve, 0.529-0.628).
conclusionsDifferences in biometric measurements between subtypes of PACD eyes were small in a population-based cohort of Chinese Americans. The poor performance of classifiers based on these measurements highlights potential challenges of developing quantitative methods to detect late PACD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.