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Diabetic retinopathy algorithms

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https://read.qxmd.com/read/30772075/a-novel-diagnostic-information-based-framework-for-super-resolution-of-retinal-fundus-images
#1
Vineeta Das, Samarendra Dandapat, Prabin Kumar Bora
Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images...
February 1, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/30744032/an-approach-to-automatic-hard-exudate-detection-in-retina-color-images-by-a-telemedicine-system-based-on-the-d-eye-sensor-and-image-processing-algorithms
#2
Emil Saeed, Maciej Szymkowski, Khalid Saeed, Zofia Mariak
Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis-50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%...
February 8, 2019: Sensors
https://read.qxmd.com/read/30707105/artificial-intelligence-for-the-detection-of-diabetic-retinopathy-in-primary-care-protocol-for-algorithm-development
#3
Josep Vidal-Alaball, Dídac Royo Fibla, Miguel A Zapata, Francesc X Marin-Gomez, Oscar Solans Fernandez
BACKGROUND: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. OBJECTIVE: The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care...
February 1, 2019: JMIR Research Protocols
https://read.qxmd.com/read/30706803/current-and-future-pharmacologic-therapies-for-diabetic-retinopathy
#4
Victor M Villegas, Stephen G Schwartz
BACKGROUND: Currently, diabetic retinopathy is the leading cause of permanent visual loss in working-age adults in industrialized nations. The chronic microangiopathic changes associated with diabetic retinopathy lead to the most common causes of severe permanent visual loss: diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR). Multiple studies have evaluated different pharmacotherapies for different levels of retinopathy. METHODS: A review of the pathophysiology of diabetic retinopathy and current and emerging pharmacotherapies for diabetic retinopathy...
January 30, 2019: Current Pharmaceutical Design
https://read.qxmd.com/read/30693385/an-effective-detection-mechanism-for-localizing-macular-region-and-grading-maculopathy
#5
C R Dhivyaa, M Vijayakumar
The eye disease is prominent in many nations including India and is said to affect up to 80% patients having diabetes. Diabetic Retinopathy is the medical term for denoting the damages to retina caused due to diabetes mellitus. Implying K means Clustering algorithm for coarse segmentation, hard distils are identified with better accuracy than the classical approaches. The variance based methods for segmenting hard distils are reviewed in the surveys and had to be improved. To remove the background features from the picture and conserve computational costs, a mathematical morphological method is used to reconstruct the image features for better segmentation...
January 28, 2019: Journal of Medical Systems
https://read.qxmd.com/read/30677193/an-ensemble-classification-of-exudates-in-color-fundus-images-using-an-evolutionary-algorithm-based-optimal-features-selection
#6
Hidayat Ullah, Tanzila Saba, Naveed Islam, Naveed Abbas, Amjad Rehman, Zahid Mehmood, Adeel Anjum
Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity...
January 24, 2019: Microscopy Research and Technique
https://read.qxmd.com/read/30629091/assessment-of-deep-generative-models-for-high-resolution-synthetic-retinal-image-generation-of-age-related-macular-degeneration
#7
Philippe M Burlina, Neil Joshi, Katia D Pacheco, T Y Alvin Liu, Neil M Bressler
Importance: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist...
January 10, 2019: JAMA Ophthalmology
https://read.qxmd.com/read/30576300/quantification-of-retinal-capillary-nonperfusion-in-diabetics-using-wide-field-optical-coherence-tomography-angiography
#8
A Yasin Alibhai, Lucas R De Pretto, Eric M Moult, Chris Or, Malvika Arya, Mitchell McGowan, Oscar Carrasco-Zevallos, ByungKun Lee, Siyu Chen, Caroline R Baumal, Andre J Witkin, Elias Reichel, Anderson Zanardi de Freitas, Jay S Duker, James G Fujimoto, Nadia K Waheed
PURPOSE: To combine advances in high-speed, wide-field optical coherence tomography angiography (OCTA) with image processing methods for semiautomatic quantitative analysis of capillary nonperfusion in patients with diabetic retinopathy (DR). METHODS: Sixty-eight diabetic patients (73 eyes), either without retinopathy or with different degrees of retinopathy, were prospectively recruited for volumetric swept-source OCTA imaging using 12 mm × 12 mm fields centered at the fovea...
December 18, 2018: Retina
https://read.qxmd.com/read/30570648/visualizing-deep-learning-models-for-the-detection-of-referable-diabetic-retinopathy-and-glaucoma
#9
Stuart Keel, Jinrong Wu, Pei Ying Lee, Jane Scheetz, Mingguang He
Importance: Convolutional neural networks have recently been applied to ophthalmic diseases; however, the rationale for the outputs generated by these systems is inscrutable to clinicians. A visualization tool is needed that would enable clinicians to understand important exposure variables in real time. Objective: To systematically visualize the convolutional neural networks of 2 validated deep learning models for the detection of referable diabetic retinopathy (DR) and glaucomatous optic neuropathy (GON)...
December 20, 2018: JAMA Ophthalmology
https://read.qxmd.com/read/30553900/using-a-deep-learning-algorithm-and-integrated-gradients-explanation-to-assist-grading-for-diabetic-retinopathy
#10
Rory Sayres, Ankur Taly, Ehsan Rahimy, Katy Blumer, David Coz, Naama Hammel, Jonathan Krause, Arunachalam Narayanaswamy, Zahra Rastegar, Derek Wu, Shawn Xu, Scott Barb, Anthony Joseph, Michael Shumski, Jesse Smith, Arjun B Sood, Greg S Corrado, Lily Peng, Dale R Webster
OBJECTIVE: To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings. DESIGN: Evaluation of diagnostic technology PARTICIPANTS: 1,796 retinal fundus images from 1,612 diabetic patients. METHODS: 10 ophthalmologists (5 general ophthalmologists, 4 retina specialists, 1 retina fellow) read images for DR severity based on the International Clinical Diabetic Retinopathy disease severity scale in one of 3 conditions: Unassisted, Grades Only, or Grades + Heatmap...
November 28, 2018: Ophthalmology
https://read.qxmd.com/read/30511282/exporting-diabetic-retinopathy-images-from-va-vista-imaging-for-research
#11
Peter Kuzmak, Charles Demosthenes, April Maa
The US Department of Veterans Affairs has been acquiring store and forward digital diabetic retinopathy surveillance retinal fundus images for remote reading since 2007. There are 900+ retinal cameras at 756 acquisition sites. These images are manually read remotely at 134 sites. A total of 2.1 million studies have been performed in the teleretinal imaging program. The human workload for reading images is rapidly growing. It would be ideal to develop an automated computer algorithm that detects multiple eye diseases as this would help standardize interpretations and improve efficiency of the image readers...
December 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://read.qxmd.com/read/30460869/optical-coherence-tomography-angiography-octa-flow-speed-mapping-technology-for-retinal-diseases
#12
Malvika Arya, Ramy Rashad, Osama Sorour, Eric M Moult, James G Fujimoto, Nadia K Waheed
Optical coherence tomography angiography (OCTA) is a noninvasive imaging modality for depth-resolved visualization of retinal vasculature. Angiographic data couples with structural data to generate a cube scan, from which en-face images of vasculature can be obtained at various axial positions. OCTA has expanded understanding of retinal vascular disorders and has primarily been used for qualitative analysis. Areas Covered: Recent studies have explored the quantitative properties of OCTA, which would allow for objective assessment and follow-up of retinal pathologies...
November 21, 2018: Expert Review of Medical Devices
https://read.qxmd.com/read/30448367/diabetic-retinopathy-techniques-in-retinal-images-a-review
#13
REVIEW
Nadeem Salamat, Malik M Saad Missen, Aqsa Rashid
The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy...
November 15, 2018: Artificial Intelligence in Medicine
https://read.qxmd.com/read/30441124/oct-fluid-segmentation-using-graph-shortest-path-and-convolutional-neural-network
#14
Abdolreza Rashno, Dara D Koozekanani, Keshab K Parhi
Diagnosis and monitoring of retina diseases related to pathologies such as accumulated fluid can be performed using optical coherence tomography (OCT). OCT acquires a series of 2D slices (Bscans). This work presents a fully-automated method based on graph shortest path algorithms and convolutional neural network (CNN) to segment and detect three types of fluid including sub-retinal fluid (SRF), intra-retinal fluid (IRF) and pigment epithelium detachment (PED) in OCT Bscans of subjects with age-related macular degeneration (AMD) and retinal vein occlusion (RVO) or diabetic retinopathy...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30440966/multi-cell-multi-task-convolutional-neural-networks-for-diabetic-retinopathy-grading
#15
Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu
Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30440508/exudates-segmentation-using-fully-convolutional-neural-network-and-auxiliary-codebook
#16
Piotr Chudzik, Bashir Al-Diri, Francesco Caliva, Giovanni Ometto, Andrew Hunter
Diabetic retinopathy (DR) is an asymptotic complication of diabetes and the leading cause of preventable blindness in the working-age population. Early detection and treatment of DR is critical to avoid vision loss. Exudates are one of the earliest and most prevalent signs of DR. In this work, we propose a novel two-stage method for the detection and segmentation of exudates in fundus photographs. In the first stage, a fully convolutional neural network architecture is trained to segment exudates using small image patches...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30440269/detection-of-diabetes-by-macrovascular-tortuosity-of-superior-bulbar-conjunctiva
#17
K A Iroshan, A D N De Zoysa, C L Warnapura, M A Wijesuriya, S Jayasinghe, N D Nanayakkara, A C De Silval
More than 8% of world population have diabetes which causes long term complications such as retinopathy, neuropathy, nephropathy and foot ulcers. Growing patient numbers has prompted large scale screening methods to detect early symptoms of diabetes (rather than elevated blood glucose levels which is a late symptom). Vascular tortuosity (twisted and curved nature of blood vessels) in retinal fundus images has proven to reflect the effect of diabetes on macrovasculature. However, large scale patient screening using retinal fundus images has limitations due to the requirement of a retinal camera...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://read.qxmd.com/read/30409338/screen-dr-collaborative-platform-for-diabetic-retinopathy
#18
Micael Pedrosa, Jorge Miguel Silva, João Figueira Silva, Sérgio Matos, Carlos Costa
BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the disease since the initial stages are asymptomatic. Most of these examinations reflect negative cases and many have poor image quality, representing an important inefficiency factor. The SCREEN-DR project aims to tackle this limitation, by researching and developing computer-aided methods for diabetic retinopathy detection...
December 2018: International Journal of Medical Informatics
https://read.qxmd.com/read/30401899/fundus-photograph-based-deep-learning-algorithms-in-detecting-diabetic-retinopathy
#19
REVIEW
Rajiv Raman, Sangeetha Srinivasan, Sunny Virmani, Sobha Sivaprasad, Chetan Rao, Ramachandran Rajalakshmi
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very less human interference. In effect, convolutional neural networks (a deep learning method) have been taught to recognize pathological lesions from images. Diabetes has high morbidity, with millions of people who need to be screened for diabetic retinopathy (DR)...
November 6, 2018: Eye
https://read.qxmd.com/read/30383712/quantitative-optical-coherence-tomography-angiography-features-for-objective-classification-and-staging-of-diabetic-retinopathy
#20
Minhaj Alam, Yue Zhang, Jennifer I Lim, Robison V P Chan, Min Yang, Xincheng Yao
PURPOSE: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging. METHODS: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans...
October 31, 2018: Retina
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