Dataset Information

Cluster analysis reveals patterns of age-related change in anterior chamber depth for gender and ethnicity: clinical implications.

ABSTRACT:

Purpose

To identify patterns of age-, gender- and refractive- related changes in Scheimpflug-based anterior chamber depth across the central 8 mm of chamber width, to derive normative models, potentially useful for angle closure disease diagnosis.

Methods

This was a retrospective, cross-sectional study. Scheimpflug photography was used to obtain anterior chamber depth measurements at 57 points across the central 8 mm of the chamber width from one eye of each healthy subject (male Caucasians (n = 189), female Caucasians (n = 186), male Asians (n = 165) and female Asians (n = 181)). Sliding window and nonlinear regression analysis was used to identify the age-related changes in chamber depth. Hierarchical cluster analysis was used to identify test locations with statistically identical age-related shifts, which were used to perform age-correction for all subjects, resulting in normative distributions of chamber depth across the chamber width. The model was examined with and without the contribution of spherical equivalent refractive error.

Results

Distinct clusters, demonstrating statistically indistinguishable age-related changes of chamber depth over time, were identified. These age-related changes followed a nonlinear regression (fifth or sixth order polynomial). Females tended to have a greater rate of chamber depth shallowing. Incorporating refractive error into the model produced minimal changes to the fit relative to the ground truth. Comparisons with cut-offs for angle closure from the literature showed that ageing alone was insufficient for identifying angle closure disease.

Conclusions

Age-, ethnicity- and gender-related differences need to be acknowledged in order to utilise anterior chamber depth data for angle closure disease diagnosis correctly. Ageing alone does not adequately account for the angle closure disease process.

SUBMITTER: Phu J

PROVIDER: S-EPMC7540376 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Cluster analysis reveals patterns of age-related change in anterior chamber depth for gender and ethnicity: clinical implications.

Phu Jack J Tong Janelle J Zangerl Barbara B Le Janet Ly JL Kalloniatis Michael M

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) 20200709 5

<h4>Purpose</h4>To identify patterns of age-, gender- and refractive- related changes in Scheimpflug-based anterior chamber depth across the central 8 mm of chamber width, to derive normative models, potentially useful for angle closure disease diagnosis.<h4>Methods</h4>This was a retrospective, cross-sectional study. Scheimpflug photography was used to obtain anterior chamber depth measurements at 57 points across the central 8 mm of the chamber width from one eye of each healthy subject (male ...[more]

PMID: 32644209

Similar Datasets

Project description:PurposeTo develop a deep learning (DL) algorithm for predicting anterior chamber depth (ACD) from smartphone-acquired anterior segment photographs.MethodsFor algorithm development, we included 4,157 eyes from 2,084 Chinese primary school students (aged 11-15 years) from Mojiang Myopia Progression Study (MMPS). All participants had with ACD measurement measured with Lenstar (LS 900) and anterior segment photographs acquired from a smartphone (iPhone Xs), which was mounted on slit lamp and under diffuses lighting. The anterior segment photographs were randomly selected by person into training (80%, no. of eyes = 3,326) and testing (20%, no. of eyes = 831) dataset. We excluded participants with intraocular surgery history or pronounced corneal haze. A convolutional neural network was developed to predict ACD based on these anterior segment photographs. To determine the accuracy of our algorithm, we measured the mean absolute error (MAE) and coefficient of determination (R 2) were evaluated. Bland Altman plot was used to illustrate the agreement between DL-predicted and measured ACD values.ResultsIn the test set of 831 eyes, the mean measured ACD was 3.06 ± 0.25 mm, and the mean DL-predicted ACD was 3.10 ± 0.20 mm. The MAE was 0.16 ± 0.13 mm, and R 2 was 0.40 between the predicted and measured ACD. The overall mean difference was -0.04 ± 0.20 mm, with 95% limits of agreement ranging between -0.43 and 0.34 mm. The generated saliency maps showed that the algorithm mainly utilized central corneal region (i.e., the site where ACD is clinically measured typically) in making its prediction, providing further plausibility to the algorithm's prediction.ConclusionsWe developed a DL algorithm to estimate ACD based on smartphone-acquired anterior segment photographs. Upon further validation, our algorithm may be further refined for use as a ACD screening tool in rural localities where means of assessing ocular biometry is not readily available. This is particularly important in China where the risk of primary angle closure disease is high and often undetected.

Dataset Information

Cluster analysis reveals patterns of age-related change in anterior chamber depth for gender and ethnicity: clinical implications.

Purpose

Methods

Results

Conclusions

Publications

Cluster analysis reveals patterns of age-related change in anterior chamber depth for gender and ethnicity: clinical implications.

Similar Datasets

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