Enhancing Image Characteristics of Retinal Images of Aggressive Posterior Retinopathy of Prematurity Using a Novel Software, (RetiView)

Jayadev, Chaitra; Vinekar, Anand; Mohanachandra, Poornima; Desai, Samit; Suveer, Amit; Mangalesh, Shwetha; Bauer, Noël; Shetty, Bhujang

doi:10.1155/2015/898197

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…This could go a long way in reducing the economic burden and increasing compliance among mothers of ROP infants. With advances in automated imaging reading softwares,[3132] it may be possible, in the future, to predict which infants will progress to treatment-requiring disease based on metrics on these vascular patterns and which will spontaneously resolve. A prospective study involving multiple ethnicities and a lower range of birth weight and gestational ages would be needed to study the correlation of early retinal immaturity with the course of ROP.…”

Section: Resultsmentioning

confidence: 99%

Retinal immaturity at first screening and retinopathy of prematurity: Image-based validation of 1202 eyes of premature infants to predict disease progression

Jayadev

Vinekar

Bharamshetter

et al. 2019

Indian J Ophthalmol

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Purpose: To use the extent of retinal immaturity at the first visit to predict progression to any stage and treatment-requiring retinopathy of prematurity (ROP). Methods: Retrospective, multicenter, nonrandomized, observational, clinical, validation study. In all, 601 Asian Indian preterm infants born < 2000 g and/or < 34 weeks of gestation completing ROP screening with RetCam images taken during each visit were included. A total of 1202 eyes of these infants were classified into three groups based on the retinal immaturity at the first screening visit into “mild” (Group 1), vessels reaching the posterior boundary of zone 3; “moderate” (Group 2), vessels entering zone 2 anterior; and “severe” (Group 3), vessels in zone 1 or zone 2 posterior. RetCam images at each subsequent visit were evaluated and the proportion of eyes that progressed to Type 1 or Type 2 ROP was correlated with the degree of retinal immaturity. Results: Of the 958 eyes in Group 1, 200 eyes in Group 2, and 44 eyes in Group 3, any stage ROP developed in 15% of eyes in Group 1, 46.5% of eyes in Group 2, and 100% of eyes in Group 3 ( P < 0.001). Sixteen of 128 eyes (12.5%), 12 of 72 (16.6%), and 28 of 44 of eyes (63.6%) in Groups 1, 2, and 3, respectively, required treatment ( P < 0.001). Conclusion: Retinal immaturity at first screening visit predicts Type 1 and Type 2 ROP. “Severe” immaturity is more likely to progress to “treatment-requiring” disease. This could be a useful tool for prognostication, counseling, and scheduling follow-up.

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Section: Resultsmentioning

confidence: 99%

Retinal immaturity at first screening and retinopathy of prematurity: Image-based validation of 1202 eyes of premature infants to predict disease progression

Jayadev

Vinekar

Bharamshetter

et al. 2019

Indian J Ophthalmol

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“…The key factors of the device are photo documentation, serial imaging, portability, and financial affordability [52]. The clinically challenging features of neonatal fundus images were enhanced with the aid of inexpensive and noninvasive RetiView software specifically in the subset of Aggressive Posterior Retinopathy of Prematurity (APROP) images [53].…”

Section: A Image Acquisitionmentioning

confidence: 99%

Automated Staging and Grading for Retinopathy of Prematurity on Indian Database

KADGE,

Nalbalwar,

Nandgaokar

et al. 2023

JAIT

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Retinopathy of prematurity (ROP) is a disorder of the retina in neonates. If ROP is not treated at early stage, neonates’ vision is affected, leading to blindness. It is necessary to diagnose and treat ROP at earliest. Several ROP assessment techniques based on Image analysis have been introduced in recent years. These studies identify only normal, abnormal and plus disease. This research article explores the identification of distinct ROP stages along with normal and abnormal detection. Detecting the stages will help to expedite the treatment and prevent vision loss. The proposed framework consists of feature extraction using Scale Invariant Feature Transform (SIFT) and Pyramid Histogram of Words (PHOW) techniques. Three efficient supervised machine learning algorithms, namely random forest (RF), support vector machine (SVM) and extreme boosting gradient (XGBoost), are used to classify different stages of ROP. A data set captured by RetCam 3 is used to evaluate the model. Based on rigorous evaluation, the accuracy of different ROP stages is 93.68%, 83.33%, 85.71%, 55.55% and 100% for normal, stage 1, 2, 3 and 4, respectively.

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“…[ 35 36 37 ] Software based image-enhancing tools, machine learning and auto detection of disease have been integrated into tele-screening programs. [ 38 39 40 41 ] Newer tools using artificial intelligence (AI) are also being evaluated and hold promise. [ 42 ] Tele-ROP has been reported in universal newborn eye screening in India[ 43 ] and is already included in India‘s national ROP guidelines.…”

Section: Fundus Photography (Fp)mentioning

confidence: 99%

Imaging the pediatric retina: An overview

Agarwal¹,

Vinekar

Chandra

et al. 2021

Indian J Ophthalmol

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Recent decade has seen a shift in the causes of childhood blinding diseases from anterior segment to retinal disease in both developed and developing countries. The common retinal disorders are retinopathy of prematurity and vitreoretinal infections in neonates, congenital anomalies in infants, and vascular retinopathies including type 1 diabetes, tumors, and inherited retinal diseases in children (up to 12 years). Retinal imaging helps in diagnosis, management, follow up and prognostication in all these disorders. These imaging modalities include fundus photography, fluorescein angiography, ultrasonography, retinal vascular and structural studies, and electrodiagnosis. Over the decades there has been tremendous advances both in design (compact, multifunctional, tele-consult capable) and technology (wide- and ultra-wide field and noninvasive retinal angiography). These new advances have application in most of the pediatric retinal diseases though at most times the designs of new devices have remained confined to use in adults. Poor patient cooperation and insufficient attention span in children demand careful crafting of the devices. The newer attempts of hand-held retinal diagnostic devices are welcome additions in this direction. While much has been done, there is still much to do in the coming years. One of the compelling and immediate needs is the pediatric version of optical coherence tomography angiography. These needs and demands would increase many folds in future. A sound policy could be the simultaneous development of adult and pediatric version of all ophthalmic diagnostic devices, coupled with capacity building of trained medical personnel.

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Enhancing Image Characteristics of Retinal Images of Aggressive Posterior Retinopathy of Prematurity Using a Novel Software, (RetiView)

Cited by 11 publications

References 22 publications

Retinal immaturity at first screening and retinopathy of prematurity: Image-based validation of 1202 eyes of premature infants to predict disease progression

Retinal immaturity at first screening and retinopathy of prematurity: Image-based validation of 1202 eyes of premature infants to predict disease progression

Automated Staging and Grading for Retinopathy of Prematurity on Indian Database

Imaging the pediatric retina: An overview

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