Retinal and peripapillary vascular deformations in prematurely born children aged 4–12 years with a history of retinopathy of prematurity

Lee, Ga-In; Park, Kyung-Ah; Oh, Sei Yeul; Kim, Sang Jin

doi:10.1038/s41598-023-30166-1

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…An increase in vessel density was observed in a study conducted on a mouse model of oxygen-induced retinopathy (OIR) 32 . However, our findings contradict other studies that did not observe a significant increase in vessel density between the ROP group and the normal group 33 , 34 .…”

Section: Discussioncontrasting

confidence: 99%

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

Sharafi,

Ebrahimiadib,

Roohipourmoallai

et al. 2024

Sci Rep

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The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.

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Section: Discussioncontrasting

confidence: 99%

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

Sharafi,

Ebrahimiadib,

Roohipourmoallai

et al. 2024

Sci Rep

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“…Given the proliferative nature of Plus disease, as well as vessels' dilation and tortuosity, it is hypothesized that the density of the vessels in a retina affected by Plus disease will be higher than in a healthy retina. In previous studies, the density of retinal vessels has been extensively addressed as a potential marker of ROP [25][26][27][28][29] , especially in relation to Plus disease 17,28 . Thus, in addition to assessing tortuosity and dilatation, we also looked at the vessels' density as a potential indicator of Plus disease.…”

Section: Image Selection and Analysismentioning

confidence: 99%

SmartPlus: A Computer-based Image Analysis Method to Predict Continuous-valued Vascular Abnormality Index in Retinopathy of Prematurity

Sharafi,

Ebrahimiadib,

Roohipourmoallai

et al. 2024

Preprint

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Plus disease is characterized by abnormal changes in retinal vasculature of premature infants. Presence of Plus disease is an important criterion for identifying treatment-requiring in Retinopathy of Prematurity (ROP). However, diagnosis of Plus disease has been shown to be subjective and there is wide variability in the classification of Plus disease by ROP experts, which is mainly because experts have different cut points for distinguishing the levels of vascular abnormality. This suggests that a continuous ROP Plus disease severity score may reflect more accurately the behavior of expert clinicians and may better standardize the classification. The effect of using quantitative methods and computer-based image analysis to improve the objectivity of Plus disease diagnosis have been well established. Nevertheless, the current methods are based on discrete classifications of the disease severity and lack the compatibility with the continuous nature of abnormal changes in retinal vasculatures. In this study, we developed a computer-based method that performs a quantitative analysis of vascular characteristics associated with Plus disease and utilizes them to build a regression model that outputs a continuous spectrum of Plus severity. We evaluated the proposed method against the consensus diagnosis made by four ROP experts on 76 posterior ROP images. The findings of our study indicate that our approach demonstrated a relatively acceptable level of accuracy in evaluating the severity of Plus disease, which is comparable to the diagnostic abilities of experts.

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Colorimetric Analyses of the Optic Nerve Head and Retina Indicate Increased Blood Flow After Vitrectomy

İnam,

El-Baz,

Kaplan

et al. 2024

Trans. Vis. Sci. Tech.

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Retinal and peripapillary vascular deformations in prematurely born children aged 4–12 years with a history of retinopathy of prematurity

Cited by 5 publications

References 40 publications

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

SmartPlus: A Computer-based Image Analysis Method to Predict Continuous-valued Vascular Abnormality Index in Retinopathy of Prematurity

Colorimetric Analyses of the Optic Nerve Head and Retina Indicate Increased Blood Flow After Vitrectomy

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