Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study

Oh, Ein; Yoo, Tae Keun; Park, Sohee

doi:10.1186/1472-6947-13-106

Cited by 47 publications

(53 citation statements)

References 51 publications

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“…The presence of micro-aneurysms and the relationship between the changes in the retinal vasculature calibre and the severity of DR has been recognised by many researchers [ 19 , 31 – 33 ]. However, such observations are usually after the manifestation of DR and are not suitable for early stage automated analysis, and recent efforts have been made for machine learning metadata analysis [ 34 ]. These are attempts to identify the risk of DR, but none of these studies have tested the relationship between changes to retinal vasculature when there is no visual impairment or signs of DR.…”

Section: Discussionmentioning

confidence: 99%

Relationship between diabetes and grayscale fractal dimensions of retinal vasculature in the Indian population

et al. 2014

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BackgroundDiabetes mellitus is rapidly increasing in the Indian population. The purpose of this study was to identify changes in the retinal vasculature of diabetic people, ahead of visual impairments. Grayscale Fractal Dimension (FD) analysis of retinal images was performed on people with type 2 diabetes from an Indian population.MethodsA cross-sectional study comprising 189 Optic Disc (OD) centred retinal images of healthy and diabetic individuals aged 14 to 73 years was conducted. Grayscale Box Counting FD of these retinal photographs was measured without manual supervision. Statistical analysis was conducted to determine the difference in the FD between diabetic and healthy (non-diabetic) people.ResultsThe results show that grayscale FD values for diabetic cases are higher compared to controls, irrespective of the gender. It was also observed that FD was higher for male compared with females.ConclusionsThere is difference in the grayscale fractal dimension of retinal vasculature of diabetic patients and healthy subjects, even when there is no reported retinopathy.

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Section: Discussionmentioning

confidence: 99%

Relationship between diabetes and grayscale fractal dimensions of retinal vasculature in the Indian population

et al. 2014

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“…Considering data mining and machine learning approaches, DR is the most studied field, mainly based on image processing techniques [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112]. A comprehensive review on computational methods for diabetic retinopathy was published in 2013 [99].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…Specifically, Torok et al developed a method, in which different types of data (results from tear fluid proteomics analysis and digital micro aneurysm detection on fundus images) were used as input in a Gradient Boosting Machine for DR screening, whereas Jin et al performed comprehensive proteomics analysis to identify biomarkers for DR, concluding that a four protein biomarker panel (APO4, C7, CLU, and ITIH2) is capable of detecting early stages of the disease. Oh et al [102] reported the first attempt in predicting DR using least absolute shrinkage and selection operator (LASSO) exploiting health record data. Moreover, Ibrahim et al [103] used a data adaptive neuro fuzzy inference classifier to predict diabetes maculopathy.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

970

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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“…Many previous studies have focused on automated detection of retinal diseases by using machine learning algorithms in order to analyze a large number of fundus photographs taken from retinal screening programs [ 6 , 7 ]. Various machine learning algorithms—K-nearest neighbor algorithm, Naive Bayes classifier, artificial neural network (ANN), and support vector machine (SVM)—were applied to automated retinal disease detection [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

et al. 2017

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Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

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Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study

Cited by 47 publications

References 51 publications

Relationship between diabetes and grayscale fractal dimensions of retinal vasculature in the Indian population

Relationship between diabetes and grayscale fractal dimensions of retinal vasculature in the Indian population

Machine Learning and Data Mining Methods in Diabetes Research

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

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