Automated retinopathy of prematurity screening using deep neural networks

Wang, Jianyong; Ju, Rong; Chen, Yuanyuan; Zhang, Lei; Hu, Junjie; Wu, Yu; Dong, Wentao; Zhong, Jie

doi:10.1016/j.ebiom.2018.08.033

Cited by 132 publications

(94 citation statements)

References 23 publications

(33 reference statements)

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“…To the best of our knowledge, there has been minimal research focused on comparative analysis of image features that are most critical for diagnosis. In contrast to other studies, Wang J et al developed a DL-based method and divided ROP into three grades with high sensitivity and specificity [35]. However, their system could only evaluate the severity of ROP; it could not identify finer details, such as the stage of ROP or presence of plus disease.…”

Section: Discussionmentioning

confidence: 99%

Automated identification of retinopathy of prematurity by image-based deep learning

et al. 2020

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Background: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis. This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment. Methods: A total of 36,231 fundus images were labeled by 13 licensed retinal experts. A 101-layer convolutional neural network (ResNet) and a faster region-based convolutional neural network (Faster-RCNN) were trained for image classification and identification. We applied a 10-fold cross-validation method to train and optimize our algorithms. The accuracy, sensitivity, and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system. The performance of the system was compared with results obtained by two retinal experts. Moreover, the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN. Results: The system achieved an accuracy of 0.903 for the ROP severity classification. Specifically, the accuracies in discriminating normal, mild, semi-urgent, and urgent were 0.883, 0.900, 0.957, and 0.870, respectively; the corresponding accuracies of the two experts were 0.902 and 0.898. Furthermore, our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease; the accuracies in discriminating stage I to stage V were 0.876, 0.942, 0.968, 0.998 and 0.999, respectively. Conclusions: Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity. The performance of the system was comparable to or better than that of human experts, demonstrating that this system could be used to support clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Automated identification of retinopathy of prematurity by image-based deep learning

et al. 2020

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“…In similar work Wang et al. (), the authors developed two networks; the first was applied to categorize an image as normal or suspicious, and the second used to further analyse suspicious images and evaluate the severeness of the ROP. The authors did not differentiate preplus and plus.…”

Section: Discussionmentioning

confidence: 99%

Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks

Mao

Luo

Liu

et al. 2019

Acta Ophthalmologica

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Background: The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of the disease, which helps the physicians to make the best judgement and communicate the decisions. Methods: The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, plus disease was classified and tortuosity, width, fractal dimension and vessel density were evaluated automatically. Results: Thetrainednetworkachievedasensitivityof95.1%with97.8%specificityfor the diagnosis of plus disease. For detection of preplus or worse, the sensitivity and specificity were 92.4% and 97.4%. The quadratic weighted k was 0.9244. The tortuosities for the normal, preplus and plus groups were 3.61 AE 0.08, 5.95 AE 1.57 and 10.67 AE 0.50 (10 4 cm À3 ). The widths of the blood vessels were 63.46 AE 0.39, 67.21 AE 0.70 and 68.89 AE 0.75 lm. The fractal dimensions were 1.18 AE 0.01, 1.22 AE 0.01 and 1.26 AE 0.02. The vessel densities were 1.39 AE 0.03, 1.60 AE 0.01 and 1.64 AE 0.09 (%). All values were statistically different among the groups. After treatment for plus disease with ranibizumab injection, quantitative analysis showed significant changes in the pathological features. Conclusions: Our system achieved high accuracy of diagnosis of plus disease in retinopathy of prematurity. It provided a quantitative analysis of the dynamic features of the disease progression. This automated system can assist physicians by providing a classification decision with auxiliary quantitative evaluation of the typical pathological features of the disease.

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“…59 Wang et al also developed two deep neural networks, Id-Net and Gr-Net, which were, respectively, designed for the identification and grading of ROP. 62 Id-Net achieved a sensitivity of 96.62% and specificity of 99.32% for identification of any ROP and Gr-Net achieved 88.46% sensitivity and 92.31% specificity for grading of ROP severity, which was comparable with three expert graders.…”

Section: Automated Image Analysismentioning

confidence: 66%

Telemedicine for Retinopathy of Prematurity

Brady

D'Amico

Campbell

2020

Telemedicine and e-Health

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Background: Retinopathy of prematurity (ROP) is a disease of the retinal vasculature that remains a leading cause of childhood blindness worldwide despite improvements in the systemic care of premature newborns. Screening for ROP is effective and cost-effective, but in many areas, access to skilled examiners to conduct dilated examinations is poor. Remote screening with retinal photography is an alternative strategy that may allow for improved ROP care. Methods: The current literature was reviewed to find clinical trials and expert consensus documents on the state-of-the-art of telemedicine for ROP. Results: Several studies have confirmed the utility of telemedicine for ROP. In addition, several clinical studies have reported favorable long-term results. Many investigators have reinforced the need for detailed protocols on image acquisition and image interpretation. Conclusions: Telemedicine for ROP appears to be a viable alternative to live ophthalmoscopic examinations in many circumstances. Standardization and documentation afforded by telemedicine may provide additional benefits to providers and their patients. With continued improvements in image quality and affordability of imaging systems as well as improved automated image interpretation tools anticipated in the near future, telemedicine for ROP is expected to play an expanding role for a uniquely vulnerable patient population.

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Automated retinopathy of prematurity screening using deep neural networks

Cited by 132 publications

References 23 publications

Automated identification of retinopathy of prematurity by image-based deep learning

Automated identification of retinopathy of prematurity by image-based deep learning

Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks

Telemedicine for Retinopathy of Prematurity

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