An automated retinal imaging method for the early diagnosis of diabetic retinopathy

Franklin, S. Wilfred; Rajan, S. Edward

doi:10.3233/thc-130759

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Franklin et al. ( 26 ) developed a novel automatic blood vessel segmentation method for fundus images that separated each image pixel into blood and non-blood vessels for automatic recognition of microaneurysms in retinal images. The detection of retinal microaneurysms using a multilayer perceptron neural network is helpful for ophthalmologists in diagnosis, and follow-up DR.…”

Section: Discussionmentioning

confidence: 99%

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Wang

Zuo

et al. 2022

Front. Endocrinol.

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BackgroundArtificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI’s general applications and research frontiers in the detection and gradation of DR.MethodsCitation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.ResultsOverall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled “automated detection” was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.ConclusionInitial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

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Section: Discussionmentioning

confidence: 99%

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Wang

Zuo

et al. 2022

Front. Endocrinol.

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“…В свое время это понимание привело медицинское сообщество к созданию специфических математических моделей, работа которых была основана на использовании большого числа признаков. Главным преимуществом, которого добились исследователи, применившие этот подход, стала возможность получать прогноз, предопределяющий непосредственно форму течения инсульта [3] Благодаря уникальным функциям, таким как обработка больших массивов данных, анализ слабоструктурированных и плохо формализованных задач, установление скрытых и сложных взаимосвязей связей между объектами исследований, искусственные нейронные сети имеют высокую значимость в медицинской отрасли. Решение задач по диагностике различных нозологических форм заболеваний, прогнозирование течения и осложнений различных патологий -это лишь немногие примеры широкого использования нейронных сетей.…”

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Experience in Using Neural Networks to Predict the Outcomes of Ischemic Stroke: A Literature Review

Dvorzhak¹,

Владимирович²,

Vansovskaya³

2021

PH&LE

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Introduction. Ischemic stroke is a structurally complex disease based on various pathogenetic mechanisms. In view of the complexity of this pathology and its structure, the medical community has established various assessment scales based on different signs. The scales were created in order to predict possible conditions of a patient at various stages of treatment. The objective of our research was to determine the relevance of applying the system of predicting outcomes of ischemic stroke based on neural networks to improve ischemic stroke treatment and management. Materials and methods: We reviewed scientific and medical literature devoted to the development and use of forecasting systems based on artificial neural networks to predict outcomes of ischemic stroke and analyzed the most common assessment scales currently used in therapeutic practices. Results. The analysis of effectiveness of available scales revealed that their main drawback was a subjective component in the assessment of a patient’s condition. The use of neural networks, in its turn, minimizes the subjective component in predicting the outcome of ischemic stroke since neural networks are capable of processing large amounts of data and can, therefore, establish implicit correlation between research objects. Conclusion. The analysis of domestic and foreign literary sources proves that the presence of a forecasting system based on a neural network is a major advantage for a health care facility. Yet, neural networks have not fully passed clinical trials that would confirm their superiority over current methods of predicting disease outcomes, which impedes their extensive use in clinical practice.

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“…Illness such as diabetes can influence vision, but even without illness eye sight is influenced by fatigue. [6][7][8] Much research effort has been dedicated to evaluate and compare the imaging technology (captors, processing display), for instance for the detection of dental caries, endodontic pathologies or periodontal diseases. [9][10][11] However, being one of the main links in the diagnostic chain, the observer has received little attention.…”

mentioning

confidence: 99%

Grey value contrast sensitivity of dental practitioners in function of luminance and age

Jacquet¹,

Cleymaet²,

Bottenberg³

2019

Dentomaxillofacial Radiology

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Objectives:Grey value perception is important in viewing and interpreting X-ray images. It is possible that ageing decreases the number of gray values that a person can distinguish. This hypothesis was tested in a group of dental practitioners from students to experienced dentists using a 12-bit grey scale.Methods and materials:A custom-made computer software was used based on the principle of the “just noticeable difference” (JND). Observers were shown a picture of an outer square with increasing grey value in which a smaller square of different grey value popped up in changing positions. As soon as the observers could see the difference, they clicked the inner square. The grey values were shown with a 12-bit depth in batches of 400 grey value steps. 59 dentists of 3 age groups (20–24, 25–45 and 46–70 years) participated in the study. A subgroup of 20 practitioners performed a validation test with test-retest and test in reversed grey value sequence (white to black). JND was calculated and plotted against grey value using a third-order polynomial function. These curves were compared statistically.Results:Test-retest, also in reversed order did not yield different curves (p values between 0.79 and 0.97). Curves between different age groups showed significant differences, with older practitioners needing more contrast to accomplish the task. Contrast sensitivity showed an optimum in the darker third of the grey scale (values around 1200).Conclusion:Age and grey value played a significant role in grey value perception by dental practitioners.

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An automated retinal imaging method for the early diagnosis of diabetic retinopathy

Cited by 7 publications

References 26 publications

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

Experience in Using Neural Networks to Predict the Outcomes of Ischemic Stroke: A Literature Review

Grey value contrast sensitivity of dental practitioners in function of luminance and age

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