scholarly journals Sensitivity and specificity of Norwegian optometrists’ evaluation of diabetic retinopathy in single-field retinal images – a cross-sectional experimental study

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Vibeke Sundling ◽  
Pål Gulbrandsen ◽  
Jørund Straand
Keyword(s):  
Experimental Study ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Retinal Images ◽  
Cross Sectional ◽  
Single Field

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

scholarly journals Diabetic Retinopathy Screening Ratio Is Improved When Using a Digital, Nonmydriatic Fundus Camera Onsite in a Diabetes Outpatient Clinic

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pia Roser ◽  
Hannes Kalscheuer ◽  
Jan B. Groener ◽  
Daniel Lehnhoff ◽  
Roman Klein ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Outpatient Clinic ◽  
Microvascular Complications ◽  
Diabetic Polyneuropathy ◽  
Cross Sectional Study ◽  
Newly Diagnosed ◽  
Cross Sectional ◽  
Fundus Camera ◽  
Diabetic Retinopathy Screening ◽  
Single Field

Objective. To evaluate the effect of onsite screening with a nonmydriatic, digital fundus camera for diabetic retinopathy (DR) at a diabetes outpatient clinic.Research Design and Methods. This cross-sectional study included 502 patients, 112 with type 1 and 390 with type 2 diabetes. Patients attended screenings for microvascular complications, including diabetic nephropathy (DN), diabetic polyneuropathy (DP), and DR. Single-field retinal imaging with a digital, nonmydriatic fundus camera was used to assess DR. Prevalence and incidence of microvascular complications were analyzed and the ratio of newly diagnosed to preexisting complications for all entities was calculated in order to differentiate natural progress from missed DRs.Results. For both types of diabetes, prevalence of DR was 25.0% (n=126) and incidence 6.4% (n=32) (T1DM versus T2DM: prevalence: 35.7% versus 22.1%, incidence 5.4% versus 6.7%). 25.4% of all DRs were newly diagnosed. Furthermore, the ratio of newly diagnosed to preexisting DR was higher than those for DN (p=0.12) and DP (p=0.03) representing at least 13 patients with missed DR.Conclusions. The results indicate that implementing nonmydriatic, digital fundus imaging in a diabetes outpatient clinic can contribute to improved early diagnosis of diabetic retinopathy.

DETECTION AND LOCALIZATION OF RETINAL EXUDATES FOR DIABETIC RETINOPATHY

2015 ◽  
Vol 23 (02) ◽  
pp. 195-212 ◽  
Author(s):  
G. MAHENDRAN ◽  
R. DHANASEKARAN
Keyword(s):  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Retinal Images ◽  
Chemical Solution ◽  
Automated Method ◽  
Retinal Capillary ◽  
Fcm Clustering ◽  
Image Processing Algorithms ◽  
Detection And Localization ◽  
Retinal Exudates

Diabetic retinopathy (DR) is a complication of diabetes caused by changes in the blood vessels of the retina. Initially, the DR causes trivial changes in the retinal capillary. The symptoms can blur or distort patients' vision, which are the main causes of blindness. The DR is characterized by the presence of exudates at the nonproliferative stage. Once damaged by DR, the effects will be permanent and hence an earlier treatment is considered as vital. The presence of exudates is detected by ophthalmologists from the dilated retinal images, which are captured by dropping chemical solution into the patient's eye that leads to irritation. Therefore, there is a need for an alternative method toward the detection of exudates using image processing algorithms from the nondilated images. In this paper, an automated method is proposed for the detection of exudates using the fuzzy C-Means (FCM) clustering technique and reconstruction through a superimposition process in the absence of dilating patient's eye. The segmented result of FCM is compared with the result obtained using the Fuzzy K-Means segmentation algorithm. The sensitivity and specificity values for the exudates detection using the FCM algorithm are 87.38% and 96.94%, respectively. On the other hand, sensitivity and specificity values for the exudates detection using the K-Means algorithm are 75.04% and 93.73%, respectively.

scholarly journals Automated Detection of Diabetic Retinopathy for Early Diagnosis using Exudate Images

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1572-1576
Keyword(s):  
Diabetic Retinopathy ◽  
Early Diagnosis ◽  
Sensitivity And Specificity ◽  
Automatic Recognition ◽  
Automated Detection ◽  
Retinal Images ◽  
Feature Classification ◽  
Detection Process ◽  
Performance Of Algorithm ◽  
Retinal Exudates

Retina plays a vital character in detection of various diseases in early point such as diabetes retinopathy which can be performed by analyzing the retinal images [6]. Diseased patients have to undergo periodic screening of eye. Standouts amongst the most predominant clinical indications of diabetic retinopathy are exudates [17]. To detect diabetic retinopathy in patients the ophthalmologist inspects the exudates by Ophthalmoscopy [17] where recognition of exudates is a vital diagnostic undertaking in which computer help may assume a noteworthy job. But intrinsic characteristics of retinal images detection process is difficult for the ophthalmologists. Here, we proposed another algorithm “Superpixel Multi-Feature Classification" for the programmed automatic recognition of retinal exudates successfully and to encourage ophthalmologist to give better patient finding experiencing diabetic retinopathy, advising them the level of seriousness ahead of time. The performance of algorithm has been compared as a result, the outcomes are effective and the sensitivity and specificity for our exudates identification is 80% and 91.28%, respectively [15].

Sensitivity and specificity of digital retinal images in grading diabetic retinopathy

2004 ◽  
Vol 82 (2) ◽  
pp. 126-130 ◽  
Author(s):  
Jukka M. Saari ◽  
Paula Summanen ◽  
Tero Kivelä ◽  
K. Matti Saari
Keyword(s):  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Retinal Images

scholarly journals Gradient Descent Based Hyparameter Tuning of Xception Architecture for Diabetic Retinopathy Segmentation and Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 4255-4263
Keyword(s):  
Experimental Study ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Sensitivity And Specificity ◽  
Glucose Level ◽  
Gradient Descent ◽  
Vision Loss ◽  
Extensive Simulation

Diabetic retinopathy (DR) is a widespread difficulty of diabetes and is considered as a main reason for vision loss in all over the globe. Several difficulties of DR can be avoided by controlling blood glucose level and timely medication. In real time, it is difficult to detect the DR and consumes more time in a manual way. This paper introduces a new Gradient Descent (GD) based Hyper parameter tuned Xception model called GD-Xception model to detect and classify DR images in an effective way. The GD-Xception model involves a series of subprocesses namely preprocessing, segmentation, feature extraction and classification. A set of extensive simulation takes place to ensure the effective outcome of the presented GD-Xception model. The presented model is tested using a DR dataset from Kaggle. The extensive experimental study clearly portrayed the superior outcome of the GD-Xception model with the maximum accuracy, sensitivity and specificity of 99.39%, 98.50% and 99.62% respectively.

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