Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review

Fenner, Beau J.; Wong, Raymond Siu Ming; Lam, Wai Ching; Tan, Gary; Cheung, Chui Ming Gemmy

doi:10.1007/s40123-018-0153-7

Cited by 102 publications

(73 citation statements)

References 87 publications

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“…Recent studies with AI in the field of diabetes represent a diverse and complex set of innovative approaches that aim to transform diabetes care in four main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools 32 . AI could improve imaging techniques such as diabetic retinopathy screening 33 , because digital imaging is an aggregation of many pixels with the same processing condition. Recently, the US Food and Drug Administration (FDA) permitted the marketing of the first medical device to use AI to detect diabetic retinopathy.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Miyamoto

Yoshimoto

Ono

et al. 2019

Sci Rep

150

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Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Miyamoto

Yoshimoto

Ono

et al. 2019

Sci Rep

150

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“…23,24 More controversial is the use of ultra-wide field (UWF) imaging systems for the evaluation of additional peripheral retinal lesions with major risk of DR progression. 25,26 Regarding the screening of DME, the use of optical coherence tomography (OCT) as the first line screening tool has yet to be fully justified from the financial viewpoint 13 but it has been shown to be cost effective as a second line screening tool in those with positive 2-dimensional markers at the primary screening event. 27…”

Section: The Cost-effectiveness Of Screeningmentioning

confidence: 99%

Screening for diabetic retinopathy: new perspectives and challenges

Vujosevic

Aldington

Silva

et al. 2020

The Lancet Diabetes & Endocrinology

396

238

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Although all stages of diabetic retinopathy (DR) have been declining since 1980 in populations with improved diabetes control, the crude prevalence of visual impairment and blindness caused by DR has increased in recent years. This is mainly due to the global increase of type 2 diabetes. Screening for DR is essential to detect referable cases that need timely full ophthalmic examination and treatment in order to avoid permanent visual loss. In recent years personalized screening intervals taking into account several risk factors has been proposed with a good costeffectiveness ratios. However, limited resources are available for nationwide screening programs. New technologies such as scanning confocal ophthalmology with ultrawide field imaging and mobile hand-held devices, tele-ophthalmology for remote grading and artificial intelligence for automated detection and classification of DR are changing screening strategies and improving its cost-effectiveness. The emergent evidence that retinal imaging is useful for identifying subjects at risk of cardiovascular disease or cognitive impairment is also a challenge that could change the concept of DR screening into a more broad examination, not limited to prevent sight-threatening disease. Box. Searching strategy and selection criteria We identified references for this review through searches of PubMed and Google Scholar for articles published in English up to November 7 th , 2019. The key words used included "diabetic retinopathy", "screening of diabetic retinopathy", "retinal imaging", "retinal neurodegeneration", "tele-ophthalmology", "artificial intelligence", "diabetic complications", "diabetic retinopathy and cardiovascular disease", "diabetic retinopathy and dementia". These keywords were used as single search terms or in combination. We also searched the reference list of original articles, clinical guidelines, systematic reviews and meta-analyses for further relevant material. All the authors conducted the literature search but with pre-specified assigned areas. Articles were initially selected for inclusion on the basis of the opinion of the contributing authors, with each section covered by two or three authors with particular expertise. The selected references were reviewed by all the members of the authorship when reading successive drafts. DCCT, UKPDS Chew EY, Davis MD, Danis RP, et al.; Action to Control Cardiovascular Risk in Diabetes Eye Study Research Group. The effects of medical management on the progression of diabetic retinopathy in persons with type 2 diabetes: the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Eye Study. Ophthalmology 2014;121:2443-2451 Action to Control Cardiovascular Risk in Diabetes Follow-On (ACCORDION) Eye Study Group and the Action to Control Cardiovascular Risk in Diabetes Follow-On (ACCORDION) Study Group. Persistent Effects of Intensive Glycemic Control on Retinopathy in Type 2 Diabetes in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Follow-On Study. Diabetes Care. 2016 Jul;39(7):1089-100 Ot...

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“…However, there are still concerns on its efficiency and full proof for prevention potential. The smartphone retinal scan may not accurate and may fail to detect an early stage Proliferative retinopathy and a skin scan on melanoma [16]. The benefits and risk are most often not uniformly dispersed among stake holders.…”

Section: Efficiency and Preventionmentioning

confidence: 99%

mHealth for Health Care in India – Defining the agenda of Bioethics A meta-analysis

Jayalal¹,

Kumar²,

Ponkilas³

2020

Global Bioethics Enquiry Journal

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Delivering health care through remote monitoring wearables' and personal devices such as mobile phones are collectively termed as "mHealth". mHealth has emerged as ubiquitous, omnipresent global phenomena. Adoption of ethical priority and challenges on implementation of them are often noted but not given adequate care. Meta-analysis of research data published from 2009 to 2018 were analyzed and the ethical concerns raised were grouped into 5 grids. 1. Privacy, security and the data ownership, 2. Process of obtaining informed consent, 3. Efficiency, prevention and maleficence 4. Empowerment, beneficence 5. Access to mHealth technology and its social justice were the major 5 concerns reported in the published literatures. Based on these reported concerns and on evaluation of these problems, for effective designing and implementation of ethical principles ten recommendations, concerns and concepts are designed. The proposed recommended steps for adopting implementations and to be used in developing mHealth apps are prioritized and tabulated along with Council for International Organizations of Medical Sciences ethical guidelines. Our study resolves that, the regulatory mechanism in India is in an evolution stage and speedy constitution of regulating frame works with ethical concerns is the need of the hour before the system goes beyond repair. Bioethicists play a vital role and are exceptionally placed to bring these more conceptual and empirical issues into the public debate and extend the current discourse beyond questions of effectiveness and safety of mHealth. The ethical issues are evolving and dynamic and hence constant evaluation and appropriate recommendations should be implemented periodically.

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Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review

Cited by 102 publications

References 87 publications

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Screening for diabetic retinopathy: new perspectives and challenges

mHealth for Health Care in India – Defining the agenda of Bioethics A meta-analysis

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