Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Coyner, Aaron S.; Swan, Ryan; Campbell, J. Peter; Ostmo, Susan; Brown, James M.; Kalpathy–Cramer, Jayashree; Kim, Sang Jin; Jonas, Karyn; Chan, Robison Vernon Paul; Chiang, Michael F.; Sönmez, Kemal; Horowitz, Jason; Coki, Osode; Eccles, Cheryl-Ann; Sarna, Leora; Orlin, Anton; Berrocal, Audina M.; Negron, Catherin I.; Denser, Kimberly; Cumming, Kristi; Osentoski, Tammy; Check, Tammy; Zajechowski, Mary; Lee, Thomas; Kruger, Evan; McGovern, Kathryn; Simmons, Charles F.; Murthy, Raghu; Galvis, Sharon; Rotter, Jerome I.; Chen, Ida; Li, Xiaohui; Taylor, Kent D.; Roll, Kaye; Chang, Ken; Beers, Andrew; Erdoğmuş, Deni̇z; Ioannidis, Stratis; Martínez-Castellanos, María Ana; Salinas-Longoria, Samantha; Romero, Rafael; Arriola, Andrea; Olguin-Manríquez, Francisco; Meraz-Gutiérrez, Miroslava; Dulanto-Reinoso, Carlos M.; Montero-Mendoza, Cristina

doi:10.1016/j.oret.2019.01.015

Cited by 51 publications

(28 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there are certain limitations in these studies. First, many studies have investigated individual assessments of image quality, field of view, and laterality of the eye 12 , 29 , 35 , 36 , 38 , 39 , 49 , 51 , 55 – 58 but have not incorporated them into a single complete module. Developing individual DL algorithms for each assessment can prove effectiveness but does not align with the workflow of clinical practice—a prerequisite for successful clinical implementation.…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Yuen

Ran

Shi

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Yuen

Ran

Shi

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

show abstract

“…15 In retinopathy of prematurity (ROP), significant inter-expert variability exists in classifying disease severity, especially regarding plus disease. 16,17 AI algorithms such as the i-ROP deep learning (DL) system have demonstrated high diagnostic accuracy for ROP, [18][19][20] outperforming experts in the field, and can be used to monitor disease progression. 21,22 Automated diagnosis in diseases such as age-related macular degeneration can improve clinical workflow by reducing the workload of physicians but have yet to be implemented or evaluated in practice.…”

Section: Diagnostic Accuracy and Efficiencymentioning

confidence: 99%

Impact of Artificial Intelligence on Medical Education in Ophthalmology

Valikodath

Cole

Ting

et al. 2021

Trans. Vis. Sci. Tech.

Self Cite

View full text Add to dashboard Cite

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians' understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.

show abstract

“…Bhattacharya et al [ 41 ] proposed a health grading method based on the presence of red lesions. Fundus image quality assessment is proposed in [ 42 ], the method uses convolutions neural network (CNN) for the task. However, none of the method uses the dataset that use tortuosity for such grading.…”

Section: Related Workmentioning

confidence: 99%

Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Maji

Sekh

2020

J Med Syst

View full text Add to dashboard Cite

Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

show abstract

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Cited by 51 publications

References 28 publications

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Impact of Artificial Intelligence on Medical Education in Ophthalmology

Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Contact Info

Product

Resources

About