Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. This paper proposes the use of new novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. The evaluations show that these novel methods in comparison to traditional imaging processing contribute to higher accuracy in classifying Plus disease, Stages of ROP and Zones. We achieve accuracy of 97.65% for Plus disease, 89.44% for Stage, 90.24% for Zones with limited training dataset.Comment: 10 pages, 4 figures, 7 tables. arXiv admin note: text overlap with arXiv:1904.08796 by other author

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology

Early and late onset sepsis and retinopathy of prematurity in a cohort of preterm infants

This study investigates the impact of antenatal and postnatal infection or inflammation on the onset and progression of Retinopathy of Prematurity (ROP). We retrospectively collected clinical and demographic data of preterm infants with birth weight ≤ 1500 g or gestational age < 30 weeks admitted to the neonatal intensive care unit of Verona from 2015 to 2019. Uni- and multivariable analysis was performed to evaluate the potential effect of selected variables on the occurrence of any stage ROP and its progression to severe ROP, defined as ROP requiring treatment. Two hundred and eighty neonates were enrolled and 60 of them developed ROP (21.4%). Oxygen need for 28 days and late-onset sepsis (LOS) increased the risk of any grade ROP after adjusting for birth weight and gestational age (OR 6.35, 95% CI 2.14-18.85 and OR 2.49, 95% CI 1.04-5.94, respectively). Days of mechanical ventilation and of non-invasive ventilation increased the risk of progression to severe ROP after adjusting for birth weight and gestational age (OR 1.08, CI 1.02-1.14 and OR 1.06, CI 1.01-1.11, respectively). Exposure to infection with production of inflammatory mediators may contribute to increase the risk of ROP occurrence in very preterm neonates

Retinal vasculature analysis: tuning and optimization for RETCAM images

This work is focused on tuning an algorithm for the automatic detection of the retinal blood vessels. Such optimization and adaptation are aimed to allow the analysis of the vessel network in preterm babies affected by Retinophathy of Prematurity. In the first chapter, the anatomical structure of the human eye is described and special attention is paid to the retina. The second chapter deals with ROP . The third chapter focuses on the segmentation of retinal images, especially in premature babies, and introduces the RetCam imaging system. In the fourth chapter the applied algorithm is defined, and the changes that have been applied to it are carefully explained. Finally, the fifth chapter presents the search results with the related conclusions and recommendations for the futur

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans

Retinopathy of prematurity screening criteria and work load implications at Tygerberg Children's Hospital, South Africa: A cross-sectional study

High-income country ROP Screening guidelines are not appropriate for middle-income countries and screening requirements may vary even between units within one city. This study aimed to determine optimal ROP screening criteria, and its workload implications, for Tygerberg Children's Hospital (TCH), South Africa. Methods This cross-sectional study included premature infants screened for ROP, at TCH (1 January 2009 to 31 December 2014). Logistic regression for prediction and classification were performed. Predictors were birth weight (BW) and gestational age (GA). Endpoints were clinically significant ROP (CSROP) and Type 1 ROP (T1ROP)