Background Non-alcoholic fatty liver disease (NAFLD) is increasingly widespread with an overall global estimated prevalence of 25%. Type 2 diabetes Mellitus (T2DM) is a key contributor to NAFLD progression and predicts moderate-severe liver fibrosis and mortality. However, there is currently no uniform consensus on routine NAFLD screening among T2DM patients, and the risk factors of NAFLD and advanced fibrosis among T2DM patients remain to be clarified fully. Aim We explored the prevalence, clinical spectrum, and risk factors of NAFLD and liver fibrosis among T2DM patients. Methods This is a cross-sectional study that enrolled subjects from a primary care clinic and a diabetes centre in Singapore. Subjects aged 21 to 70 years of all ethnic groups with an established T2DM diagnosis were included. Subjects with chronic liver diseases of other aetiologies were excluded. All subjects underwent transient elastography for hepatic steatosis and fibrosis assessment. Their demographics, anthropometric measurements and clinical parameters were collected. Statistical analysis was performed using STATA/SE16.0 software. Results Among 449 enrolled T2DM subjects, 436 with complete data and valid transient elastography results were analysed. Overall, 78.72% (344/436) of the T2DM subjects had NAFLD, of which 13.08% (45/344) had increased liver stiffness. Higher ALT level (OR = 1.08; 95% CI: 1.03-1.14; p = 0.004), obesity (BMI � 27.5 kg/m2, OR = 2.64; 95% CI: 1.28-5.44;

PurposeTo assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.MethodsA convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen’s kappa coefficients.ResultsThe classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890–0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort.ConclusionAn OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.

PurposeTo evaluate the performance of swept source optical coherence tomography (SS-OCT) to detect gonioscopic angle closure using different classification algorithms.MethodsThis was a cross-sectional study of 2028 subjects without ophthalmic symptoms recruited from a community-based clinic. All subjects underwent gonioscopy and SS-OCT (Casia, Tomey Corporation, Nagoya, Japan) under dark room conditions. For each eye, 8 out of 128 frames (22.5° interval) were selected to measure anterior chamber parameters namely anterior chamber width, depth, area and volume (ACW, ACD, ACA, and ACV), lens vault (LV), iris curvature (IC), iris thickness (IT) from 750 µm and 2000 µm from the scleral spur, iris area and iris volume. Five diagnostic algorithms—stepwise logistic regression, random forest, multivariate adaptive regression splines, recursive partitioning and Naïve Bayes were evaluated for detection of gonioscopic angle closure (defined as ≥2 closed quadrants). The performance of the horizontal frame was compared with that of other meridians.ResultsData from 1988 subjects, including 143 (7.2%) with gonioscopic angle closure, were available for analysis. They were divided into two groups: training (1391, 70%) and validation (597, 30%). The best algorithm for detecting gonioscopic angle closure was stepwise logistic regression with an area under the curve of 0.91 (95% CI 0.88 to 0.93) using all parameters, and 0.88 (95% CI 0.82 to 0.93) using only ACA, LV and IC of the horizontal meridian scan.ConclusionsA stepwise logistic regression model incorporating SS-OCT measurements has a high diagnostic ability to detect gonioscopic angle closure.

Background/aimsAs swept-source optical coherence tomography (SS-OCT) simultaneously obtains 128 meridional scans, it is important to identify which scans are playing the main role in classifying gonioscopic angle closure to simplify the analysis. We aimed to evaluate the diagnostic performance of every meridional scan in its ability to detect gonioscopic angle closure.MethodsObservational study with 2027 phakic subjects consecutively recruited from a community polyclinic. Gonioscopy and SS-OCT were performed. Gonioscopic angle closure was defined as non-visibility of the posterior trabecular meshwork in ≥180° of the angle, while SS-OCT was defined as iridotrabecular contact anterior to the scleral spur. The area under the receiver operating characteristic curve (AUC) was calculated to assess the diagnostic performance of each single scan, the sequential anticlockwise cumulative effect of those single scans and different combinations of them.ResultsThe AUCs of each scan ranged from 0.73 to 0.82. The single scan at 80°–260° had the highest AUC (0.82, 95% CI 0.79 to 0.84) and performed significantly better than most of the temporonasal scans (from 0° to 52° and from 153° to 179°). The superoinferior scans achieved higher AUCs compared with the temporonasal ones. When assessing the cumulative effect of adding individual scans consecutively, the peak AUC (0.80) was obtained when considering the superoinferior scans closer to 80°–85°, but no further positive cumulative effect was seen when adding the rest of the temporonasal scans of the circumference.ConclusionsIn conclusion, the single SS-OCT scan at 80°–260° had the highest diagnostic performance. Our study suggests that the 360° evaluation may not translate to better clinical utility for detection of gonioscopic angle closure.