Categorization of Diabetic Retinopathy Severity Levels of transformed images using clustering approach.

AimsTo compare microaneurysm (MA) counts using ultrawide field colour images (UWF-CI) and ultrawide field fluorescein angiography (UWF-FA).MethodsRetrospective study including patients with type 1 or 2 diabetes mellitus receiving UWF-FA and UWF-CI within 2 weeks. MAs were manually counted in individual Early Treatment Diabetic Retinopathy Study (ETDRS) and extended UWF zones. Fields with MAs ≥20 determined diabetic retinopathy (DR) severity (0 fields=mild, 1–3=moderate, ≥4=severe). UWF-FA and UWF-CI agreement was determined and UWF-CI DR severity sensitivity analysis adjusting for UWF-FA MA counts performed.ResultsIn 193 patients (288 eyes), 2.4% had no DR, 29.9% mild non-proliferative DR (NPDR), 32.6% moderate (NPDR), 22.9% severe NPDR and 12.2% proliferative DR. UWF-FA MA counts were 3.5-fold higher (p<0.001) than UWF-CI counts overall, 3.2x-fold higher in ETDRS fields (p<0.001) and 5.3-fold higher in extended ETDRS fields (p<0.001) and higher in type 1 versus type 2 diabetes (p<0.001). In eyes with NPDR on UWF-CI (n=246), UWF-FA images had 1.6x–3.5x more fields with ≥20 MAs (p<0.001). Fair agreement existed between imaging modalities (k=0.221–0.416). In ETDRS fields, DR severity agreement increased from k=0.346 to 0.600 when dividing UWF-FA counts by a factor of 3, followed by rapid decline in agreement thereafter. Total UWF area agreement increased from k=0.317 to 0.565 with an adjustment factor of either 4 or 5.ConclusionsUWF-FA detects threefold to fivefold more MAs than UWF-CI and identifies 1.6–3.5-fold more fields affecting DR severity. Differences exist at all DR severity levels, thus limiting direct comparison between the modalities. However, correcting UWF-FA MA counts substantially improves DR severity agreement between the modalities.

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DIABETIC RETINOPATHY IMAGE CLASSIFICATION USING DEEP NEURAL NETWORK

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.20512 ◽

2017 ◽

Vol 10 (13) ◽

pp. 461

Author(s):

Parvathy En ◽

Bharadwaja Kumar G

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Image Classification ◽

Vision Loss ◽

Training Model ◽

Fundus Images ◽

Global Epidemic ◽

Important Field ◽

Severity Levels ◽

Field Efficiency

Healthcare is an important field where image classification has an excellent value. An alarming healthcare problem recognized by the WHO that theworld suffers is diabetic retinopathy (DR). DR is a global epidemic which leads to the vision loss. Diagnosing the disease using fundus images is a timeconsuming task and needs experience clinicians to detect the small changes. Here, we are proposing an approach to diagnose the DR and its severity levels from fundus images using convolutional neural network algorithm (CNN). Using CNN, we are developing a training model which identifies the features through iterations. Later, this training model will classify the retina images of patients according to the severity levels. In healthcare field, efficiency and accuracy is important, so using deep learning algorithms for image classification can address these problems efficiently.

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PDB71 QUALITY-ADJUSTED LIFE-YEAR (QALY) WEIGHTS ASSOCIATED WITH DIFFERENT SEVERITY LEVELS OF DIABETIC RETINOPATHY

Value in Health ◽

10.1016/s1098-3015(11)72129-5 ◽

2010 ◽

Vol 13 (7) ◽

pp. A297

Author(s):

E Heintz ◽

Peebo B Bourghardt ◽

AB Wiréhn ◽

U Rosenqvist ◽

LÅ Levin

Keyword(s):

Diabetic Retinopathy ◽

Quality Adjusted Life Year ◽

Severity Levels ◽

Quality Adjusted Life

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Comparison of montage with conventional stereoscopic seven-field photographs for assessment of ETDRS diabetic retinopathy severity

International Journal of Retina and Vitreous ◽

10.1186/s40942-019-0201-z ◽

2019 ◽

Vol 5 (1) ◽

Author(s):

Nam V. Nguyen ◽

Erin M. Vigil ◽

Muhammad Hassan ◽

Muhammad S. Halim ◽

Sean C. Baluyot ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Severity Score ◽

Statistical Significance ◽

Complete Agreement ◽

Stereo Pair ◽

Substantial Agreement ◽

Adobe Photoshop ◽

Quality Image ◽

One Step ◽

Severity Levels

Abstract Background The ETDRS stereoscopic seven-field (7F) has been a standard imaging and grading protocol for assessment of diabetic retinopathy (DR) severity score in many clinical trials. To the best of our knowledge, the comparison between montage and stereoscopic 7F has not been reported in the literature. Therefore, the main purpose of this study is to compare agreement between montage and stereoscopic seven-field (7F) photographs in the assessment of DR severity. Methods Stereoscopic 7F photographs were captured from subjects with DR. Montages of monoscopic 7F images were created using Adobe Photoshop CS6 Extended©. The best quality image of each stereo pair was selected and placed on a 150 × 125-inch canvas field according to the standard location from field 1 to 7. All the fields were aligned following the vessels and overlaid using the built-in blending tool. The resulting montage was utilized for grading and compared with grading on stereoscopic 7F photographs. Three independent graders were asked to assess DR severity on stereoscopic 7F photographs and montage. Severity level agreement between stereo 7F and montage was cross-tabulated and the agreement of DR severity levels between stereoscopic 7-field images and montage was analyzed using κ intergrader agreement; statistical significance was set at p < 0.05. Results A total of 50 eyes were included in the study. There was a substantial agreement between stereoscopic 7F and montage (κ = 0.745, κweighted = 0.867) in assessment of DR severity. Of 50 eyes, 80% of the cases showed complete agreement, and 100% of the cases had agreement within one-step. There was a moderate agreement among graders, and κ-value ranged from 0.4705 to 0.5803. Conclusion In this study, we found a substantial agreement in assessing DR severity score employing non-stereoscopic montage and stereoscopic 7F photographs.

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Exudates as Landmarks Identified through FCM Clustering in Retinal Images

Applied Sciences ◽

10.3390/app11010142 ◽

2020 ◽

Vol 11 (1) ◽

pp. 142

Author(s):

Hadi Hamad ◽

Tahreer Dwickat ◽

Domenico Tegolo ◽

Cesare Valenti

Keyword(s):

Diabetic Retinopathy ◽

Random Selection ◽

Retinal Images ◽

Automatic Identification ◽

Fundus Images ◽

Fuzzy C Means Clustering ◽

Fcm Clustering ◽

Clustering Approach ◽

Sensitivity Specificity ◽

Selection Of

The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient’s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively.

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Using Conventional Articulation Tests With Highly Unintelligible Children

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.2301.52 ◽

1992 ◽

Vol 23 (1) ◽

pp. 52-60 ◽

Cited By ~ 1

Author(s):

Pamela G. Garn-Nunn ◽

Vicki Martin

Keyword(s):

Test Results ◽

Phonological Processes ◽

Rule Based ◽

Severity Level ◽

Error Sensitivity ◽

Conventional Tests ◽

Severity Levels ◽

Conventional Test ◽

Impaired Children

This study explored whether or not standard administration and scoring of conventional articulation tests accurately identified children as phonologically disordered and whether or not information from these tests established severity level and programming needs. Results of standard scoring procedures from the Assessment of Phonological Processes-Revised, the Goldman-Fristoe Test of Articulation, the Photo Articulation Test, and the Weiss Comprehensive Articulation Test were compared for 20 phonologically impaired children. All tests identified the children as phonologically delayed/disordered, but the conventional tests failed to clearly and consistently differentiate varying severity levels. Conventional test results also showed limitations in error sensitivity, ease of computation for scoring procedures, and implications for remediation programming. The use of some type of rule-based analysis for phonologically impaired children is highly recommended.

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