Development of a scale for grading pre-plus and plus disease using retinal images: A pilot study

Nasrazadani, David; Wallace, David K.; Freedman, Sharon F.; Prakalapakorn, S. Grace

doi:10.1016/j.jaapos.2018.01.007

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…To address this problem, many studies have worked to develop semi‐automated quantitative image analysis tools to diagnose plus disease more objectively, with results such as the ROPTool (Nasrazadani et al. ), Retinal Image MultiScale Analysis software (Gelman et al. ) and Singapore I Vessel Assessment software (School of Computing, National University of Singapore, Singapore) (Lau et al.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…They achieved an average accuracy of 80.15%. In 2018, an ROP tool was constructed using 20 images to differentiate between pre-plus and plus ROP diseases [28]. Oloumi et al utilized Gabor filters to distinguish between plus and non-plus ROP disease using 110 images of 41 patients [24,29].…”

Section: Previous Diagnostic Toolsmentioning

confidence: 99%

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Attallah

2021

Diagnostics

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Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.

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Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity

Bao

Ming

Mou

et al. 2021

Computer Methods and Programs in Biomedicine

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Development of a scale for grading pre-plus and plus disease using retinal images: A pilot study

Cited by 4 publications

References 8 publications

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

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

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity

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