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• W2169176543 abstract "ObjectiveA recent study found that a combination of 6 anterior segment optical coherence tomography (ASOCT) parameters (anterior chamber area, volume, and width [ACA, ACV, ACW], lens vault [LV], iris thickness at 750 μm from the scleral spur, and iris cross-sectional area) explain >80% of the variability in angle width. The aim of this study was to evaluate classification algorithms based on ASOCT measurements for the detection of gonioscopic angle closure.DesignCross-sectional study.ParticipantsWe included 2047 subjects aged ≥50 years.MethodsParticipants underwent gonioscopy and ASOCT (Carl Zeiss Meditec, Dublin, CA). Customized software (Zhongshan Angle Assessment Program, Guangzhou, China) was used to measure ASOCT parameters in horizontal ASOCT scans. Six classification algorithms were considered (stepwise logistic regression with Akaike information criterion, Random Forest, multivariate adaptive regression splines, support vector machine, naïve Bayes' classification, and recursive partitioning). The ASOCT-derived parameters were incorporated to generate point and interval estimates of the area under the receiver operating characteristic (AUC) curves for these algorithms using 10-fold cross-validation as well as 50:50 training and validation.Main Outcome MeasuresWe assessed ASOCT measurements and angle closure.ResultsData on 1368 subjects, including 295 (21.6%) subjects with gonioscopic angle closure were available for analysis. The mean (± standard deviation) age was 62.4±7.5 years and 54.8% were females. Angle closure subjects were older and had smaller ACW, ACA, and ACV; greater LV; and thicker irides (P<0.001 for all). For both, the 10-fold cross-validation and the 50:50 training and validation methods, stepwise logistic regression was the best algorithm for detecting eyes with gonioscopic angle closure with testing set AUC of 0.954 (95% confidence interval [CI], 0.942–0.966) and 0.962 (95% CI, 0.948–0.975) respectively, whereas recursive partitioning had relatively the poorest performance with testing set AUC 0.860 (95% CI, 0.790–0.930) and 0.905 (95% CI, 0.876–0.933), respectively. This algorithm performed similarly well (AUC, 0.957) in a second independent sample of 200 angle closure subjects and 302 normal controls.ConclusionsA classification algorithm based on stepwise logistic regression that used a combination of 6 parameters obtained from a single horizontal ASOCT scan identified subjects with gonioscopic angle closure >95% of the time.Financial Disclosure(s)The authors have no proprietary or commercial interest in any of the materials discussed in this article. A recent study found that a combination of 6 anterior segment optical coherence tomography (ASOCT) parameters (anterior chamber area, volume, and width [ACA, ACV, ACW], lens vault [LV], iris thickness at 750 μm from the scleral spur, and iris cross-sectional area) explain >80% of the variability in angle width. The aim of this study was to evaluate classification algorithms based on ASOCT measurements for the detection of gonioscopic angle closure. Cross-sectional study. We included 2047 subjects aged ≥50 years. Participants underwent gonioscopy and ASOCT (Carl Zeiss Meditec, Dublin, CA). Customized software (Zhongshan Angle Assessment Program, Guangzhou, China) was used to measure ASOCT parameters in horizontal ASOCT scans. Six classification algorithms were considered (stepwise logistic regression with Akaike information criterion, Random Forest, multivariate adaptive regression splines, support vector machine, naïve Bayes' classification, and recursive partitioning). The ASOCT-derived parameters were incorporated to generate point and interval estimates of the area under the receiver operating characteristic (AUC) curves for these algorithms using 10-fold cross-validation as well as 50:50 training and validation. We assessed ASOCT measurements and angle closure. Data on 1368 subjects, including 295 (21.6%) subjects with gonioscopic angle closure were available for analysis. The mean (± standard deviation) age was 62.4±7.5 years and 54.8% were females. Angle closure subjects were older and had smaller ACW, ACA, and ACV; greater LV; and thicker irides (P<0.001 for all). For both, the 10-fold cross-validation and the 50:50 training and validation methods, stepwise logistic regression was the best algorithm for detecting eyes with gonioscopic angle closure with testing set AUC of 0.954 (95% confidence interval [CI], 0.942–0.966) and 0.962 (95% CI, 0.948–0.975) respectively, whereas recursive partitioning had relatively the poorest performance with testing set AUC 0.860 (95% CI, 0.790–0.930) and 0.905 (95% CI, 0.876–0.933), respectively. This algorithm performed similarly well (AUC, 0.957) in a second independent sample of 200 angle closure subjects and 302 normal controls. A classification algorithm based on stepwise logistic regression that used a combination of 6 parameters obtained from a single horizontal ASOCT scan identified subjects with gonioscopic angle closure >95% of the time." @default.
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• W2169176543 date "2013-01-01" @default.
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• W2169176543 title "Classification Algorithms Based on Anterior Segment Optical Coherence Tomography Measurements for Detection of Angle Closure" @default.
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• W2169176543 cites W2031939800 @default.
• W2169176543 cites W2040799936 @default.
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• W2169176543 cites W2343720484 @default.
• W2169176543 cites W2911964244 @default.
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• W2169176543 doi "https://doi.org/10.1016/j.ophtha.2012.07.005" @default.
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