Progress of artificial intelligence in diabetic retinopathy screening

Wang, Yuelin; Yang, Jingyun; Yang, Jingyuan; Zhao, Xinyu; Chen, Youxin; Wu, Yu

doi:10.1002/dmrr.3414

Cited by 19 publications

(12 citation statements)

References 77 publications

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“…Although the “black box” character of ML systems is an epistemic hurdle for explainability, it is not for diagnosis itself ( 2 , 27 ). The more opaque an ML algorithm is, the more accurately it performs ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

et al. 2021

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Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

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Section: Discussionmentioning

confidence: 99%

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

et al. 2021

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“…There are numerous related studies; for instance, references [25][26][27] focused on the use of artificial intelligence in detecting diabetes; however, these studies mainly focused on software and used hospital data. Other studies [28][29][30] combined software and hardware, as did our study and were related to diabetes; however, they focused only on diabetic retinopathy, which is a complication of diabetes. Similar studies have attempted to use a convenient and noninvasive method to diagnose diabetes and have achieved an accuracy score higher than 0.85 [31,32].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Xiang

Shujin

Hong-fang³

et al. 2021

BioMed Research International

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In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

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“…We have collected data on the molecular mechanisms of vitamin D in oxidative stress pathways and we have reported that vitamin D plays a crucial role in protecting cells from oxidative damage. Genetic mutations in the VDR have also been linked with DR 198,199 and therefore have a protective effect on DR development. Vitamin D with VDR have a crucial effect on mitochondria because they not only control respiratory activity but also preserve mitochondrial integrity and cell survival protecting against oxidative stress 200 .…”

Section: Discussionmentioning

confidence: 99%

Protective role of vitamin D against oxidative stress in diabetic retinopathy

Valle

Russo

Malaguarnera

2021

Diabetes Metabolism Res

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Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus. There is much evidence showing that a high level of mitochondrial overproduction of reactive oxygen species in the diabetic retina contributes in modifying cellular signalling and leads to retinal cell damage and finally to the development of DR pathogenesis. In the last few decades, it has been reported that vitamin D is involved in DR pathogenesis. Vitamin D, traditionally known as an essential nutrient crucial in bone metabolism, has also been proven to be a very effective antioxidant. It has been demonstrated that it modulates the production of advanced glycosylated end products, as well as several pathways including protein kinase C, the polyol pathway leading to the reduction of free radical formation. It prevents the translocation of nuclear factor kappa B, preventing the inflammatory response, acting as an immunomodulator, and modulates autophagy and apoptosis. In this review, we explore the molecular mechanisms by which vitamin D protects the eye from oxidative stress, in order to evaluate whether vitamin D supplementation may be useful to mitigate the deleterious effects of free radicals in DR.

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Progress of artificial intelligence in diabetic retinopathy screening

Cited by 19 publications

References 77 publications

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Protective role of vitamin D against oxidative stress in diabetic retinopathy

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