Abstract
PURPOSE: To establish and evaluate algorithms for detection of primary angle closure suspects (PACS), the risk factor for primary angle closure disease by combining multiple static and dynamic anterior segment optical coherence tomography (ASOCT) parameters. METHODS: Observational, cross-sectional study. The right eyes of subjects aged ≥40 years who participated in the 5-year follow-up of the Handan Eye Study, and underwent gonioscopy and ASOCT examinations under light and dark conditions were included. All ASOCT images were analyzed by Zhongshan Angle Assessment Program. Backward logistic regression (BLR) was used for inclusion of variables in the prediction models. BLR, naïve Bayes' classification (NBC), and neural network (NN) were evaluated and compared using the area under the receiver operating characteristic curve (AUC). RESULTS: Data from 744 subjects (405 eyes with PACS and 339 normal eyes) were analyzed. Angle recess area at 750 µm, anterior chamber volume, lens vault in light and iris cross-sectional area change/pupil diameter change were included in the prediction models. The AUCs of BLR, NBC, and NN were 0.827 (95% confidence interval [CI], 0.798-0.856), 0.826 (95% CI, 0.797-0.854), and 0.844 (95% CI, 0.817-0.871), respectively. No significant statistical differences were found between the three algorithms (P = 0.622). CONCLUSIONS: The three algorithms did not meet the requirements for population-based screening of PACS. One possible reason could be the different angle closure mechanisms in enrolled eyes. TRANSLATIONAL RELEVANCE: This study provides a promise for basis for future research directed toward the development of an image-based, noncontact method to screen for angle closure.