Ophthoselfie: Detailed Self-imaging of Cornea and Anterior Segment by Smartphone.
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ABSTRACT:
SUBMITTER: Kaya A
PROVIDER: S-EPMC5468524 | biostudies-other | 2017 Jun
REPOSITORIES: biostudies-other
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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.
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Project description:To study the accommodation process in normal eyes using a commercially available clinical system based on swept-source anterior segment optical coherence tomography (AS-OCT).Ophthalmology Department, University of Parma, Italy.Evaluation of diagnostic technology.Right eyes were analyzed using swept-source AS-OCT (Casia SS-1000). The optical vergence of the internal coaxial fixation target was adjusted during imaging to obtain monocular accommodation stimuli with different amplitudes (0, 3.0, 6.0, and 9.0 diopters [D]). Overlapping of real and conjugate OCT images enabled imaging of all the anterior segment optical surfaces in a single frame. Central corneal thickness (CCT), anterior chamber depth (ACD), and lens thickness were extracted from the OCT scans acquired at different static accommodation stimulus amplitudes. The crystalline lens was analyzed dynamically during accommodation and disaccommodation by acquiring sequential OCT images of the anterior segment at a rate of 8 frames per second. The lens thickness was extracted from the temporal sequence of OCT images and plotted as a function of time.The study analyzed 14 eyes of 14 subjects aged 18 to 46 years. During accommodation, the decrease in the ACD was statistically significant (P < .05), as were the increase in the lens thickness (P < .001) and the slight movement forward of the lens central point (P < .01). The CCT and anterior chamber width measurements did not change statistically significantly during accommodation. The lens thickness at 0 D was positively correlated with age (P < .01).High-resolution real-time imaging and biometry of the accommodating anterior segment can be effectively performed using a commercially available swept-source AS-OCT clinical device.No author has a financial or proprietary interest in any material or method mentioned.
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