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      A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video)

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          Abstract

          Purpose

          The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous detachment (VD), and vitreous hemorrhage (VH) in ophthalmic ultrasound in real time.

          Methods

          We used a deep convolutional neural network to develop a deep learning system to screen multiple abnormal findings in ophthalmic ultrasonography with 3580 images for classification and 941 images for segmentation. Sixty-two videos were used as the test dataset in real time. External data containing 598 images were also used for validation. Another 155 images were collected to compare the performance of the model to experts. In addition, a study was conducted to assess the effect of the model in improving lesions recognition of the trainees.

          Results

          The model achieved 0.94, 0.90, 0.92, 0.94, and 0.91 accuracy in recognizing normal, VD, VH, RD, and other lesions. Compared with the ophthalmologists, the modal achieved a 0.73 accuracy in classifying RD, VD, and VH, which has a better performance than most experts ( P < 0.05). In the videos, the model had a 0.81 accuracy. With the model assistant, the accuracy of the trainees improved from 0.84 to 0.94.

          Conclusions

          The model could serve as a screening tool to rapidly identify patients with RD, VD, and VH. In addition, it also has potential to be a good tool to assist training.

          Translational Relevance

          We developed a deep learning model to make the ultrasound work more accurately and efficiently.

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          Most cited references 33

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          Learning Deep Features for Discriminative Localization

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            Automated Identification of Diabetic Retinopathy Using Deep Learning

            Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.
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              The Calculation of Posterior Distributions by Data Augmentation

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                Author and article information

                Journal
                Transl Vis Sci Technol
                Transl Vis Sci Technol
                tvst
                TVST
                Translational Vision Science & Technology
                The Association for Research in Vision and Ophthalmology
                2164-2591
                21 April 2021
                April 2021
                : 10
                : 4
                Affiliations
                [1 ]Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
                [2 ]Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
                [3 ]School of Resources and Environmental Sciences of Wuhan University, Wuhan, Hubei Province, China
                [4 ]Department of Ophthalmology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, China
                Author notes
                Correspondence: Yanning Yang, Department of Ophthalmology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan 430060, Hubei Province, China. e-mail: ophyyn@ 123456163.com
                [*]

                DC, YY and YZ contributed equally to this work.

                Article
                TVST-20-3242
                10.1167/tvst.10.4.22
                8083108
                Copyright 2021 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                Page count
                Pages: 8
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