Observational studyActa ophthalmologica2021
Detection of primary angleclosure suspect with different mechanisms of angle closure using multivariate prediction models.
Observational study in Acta ophthalmologica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- ACM-Assessor: An Artificial Intelligence System for Assessing Angle Closure Mechanisms in Ultrasound Biomicroscopy.Bioengineering (Basel, Switzerland) · 2025Article
- Analysis of anterior segment in primary angle closure suspect with deep learning models.BMC medical informatics and decision making · 2024Article
- Development of a β-Variational Autoencoder for Disentangled Latent Space Representation of Anterior Segment Optical Coherence Tomography Images.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeWe had found that a multivariate prediction model used for the detection of primary angle-closure suspects (PACS) by combining multiple static and dynamic anterior segment optical coherence tomography (ASOCT) parameters had an area under the receiver operating characteristic curve (AUC) of 0.844. We undertook this study to evaluate this method in screening of PACS with different dominant mechanisms of angle closure (AC).
methodsThe right eyes of subjects aged ≥40 years who participated in the 5-year follow-up of the Handan Eye Study and had undergone gonioscopy and ASOCT examinations under light and dark conditions were included. All ASOCT images were analysed by the Zhongshan Angle Assessment Program. The dominant AC mechanism in each eye was determined to be pupillary block (PB), plateau iris configuration (PIC) or thick peripheral iris roll (TPIR). Backward logistic regression (LR) was used for inclusion of variables in the prediction models. LR, Naïve Bayes' classification (NBC) and neural network (NN) were evaluated and compared using the AUC.
resultsData from 796 subjects (413 PACS and 383 normal eyes) were analysed. The AUCs of LR, NBC and NN in the PB group were 0.920, 0.918 and 0.917. The AUCs of LR, NBC and NN in the PIC group were 0.715, 0.708 and 0.707. The AUCs of LR, NBC and NN in TPIR group were 0.867, 0.833 and 0.886.
conclusionsPrediction models showed the best performance for detection of PACS with PB mechanism for AC and have potential for screening of PACS.
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