scholarly journals Blindness (Diabetic Retinopathy) Severity Scale Detection

2021 ◽  
Author(s):  
Ramya Bygari ◽  
Rachita Naik ◽  
Uday Kumar P
Keyword(s):  
Diabetic Retinopathy ◽  
Severity Scale ◽  
Scale Detection

scholarly journals Automated feature-based grading and progression analysis of diabetic retinopathy

Eye ◽  
2021 ◽  
Author(s):  
Lutfiah Al-Turk ◽  
James Wawrzynski ◽  
Su Wang ◽  
Paul Krause ◽  
George M. Saleh ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Diabetic Macular Oedema ◽  
Large Datasets ◽  
Image Analysis System ◽  
Learning Approaches ◽  
External Evaluation ◽  
Severity Scale ◽  
Grading Systems ◽  
Feature Based ◽  
Analysis System

Abstract Background In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. Methods The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines. Results External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. Conclusions We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

scholarly journals Impact of baseline Diabetic Retinopathy Severity Scale scores on visual outcomes in the VIVID-DME and VISTA-DME studies

2017 ◽  
Vol 102 (7) ◽  
pp. 954-958 ◽  
Author(s):  
Giovanni Staurenghi ◽  
Nicolas Feltgen ◽  
Jennifer J Arnold ◽  
Todd A Katz ◽  
Carola Metzig ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Group Versus ◽  
Diabetic Macular Oedema ◽  
Severity Scale ◽  
Treatment Benefit ◽  
Treatment Groups ◽  
Link Type ◽  
Intravitreal Aflibercept ◽  
Scale Scores ◽  
Consistent Treatment

Background/aimsTo evaluate intravitreal aflibercept versus laser in subgroups of patients with baseline Diabetic Retinopathy Severity Scale (DRSS) scores ≤43, 47, and ≥53 in VIVID-DME and VISTA-DME.MethodsPatients with diabetic macular oedema were randomised to receive intravitreal aflibercept 2 mg every 4 weeks (2q4), intravitreal aflibercept 2 mg every 8 weeks after five initial monthly doses (2q8), or macular laser photocoagulation at baseline with sham injections at every visit. These post hoc analyses evaluate outcomes based on baseline DRSS scores in patients in the integrated dataset. The 2q4 and 2q8 treatment groups were also pooled.Results748 patients had a baseline DRSS score based on fundus photographs (≤43, n=301; 47, n=153; ≥53, n=294). At week 100, the least squares mean difference between treatment groups (effect of intravitreal aflibercept above that of laser, adjusting for baseline best-corrected visual acuity) was 8.9 (95% CI 5.99 to 11.81), 9.7 (95% CI 5.54 to 13.91), and 11.0 (95% CI 7.96 to 14.1) letters in those with baseline DRSS scores ≤43, 47, and ≥53, respectively. The proportions of patients with ≥2 step DRSS score improvement were greater in the intravitreal aflibercept group versus laser, respectively, for those with baseline DRSS scores of ≤43 (13% vs 5.9%), 47 (25.8% vs 4.5%), and ≥53 (64.5% vs 28.4%).ConclusionsRegardless of baseline DRSS score, functional outcomes were superior in intravitreal aflibercept-treated patients, demonstrating consistent treatment benefit across various baseline levels of retinopathy.Trial registration numbersNCT01331681 and NCT01363440, Post-results.

scholarly journals Real-Time Diabetic Retinopathy Severity Score Level versus Ultra-Widefield Leakage Index-Guided Management of Diabetic Retinopathy: Two-Year Outcomes from the Randomized PRIME Trial

2021 ◽  
Vol 11 (9) ◽  
pp. 885
Author(s):  
Hannah J. Yu ◽  
Justis P. Ehlers ◽  
Duriye Damla Sevgi ◽  
Margaret O’Connell ◽  
Jamie L. Reese ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Real Time ◽  
Severity Score ◽  
Imaging Biomarkers ◽  
Prime Trial ◽  
Severity Scale ◽  
Intravitreal Aflibercept ◽  
Level Versus ◽  
Pro Re Nata

The prospective PRIME trial applied real-time, objective imaging biomarkers to determine individualized retreatment needs with intravitreal aflibercept injections (IAI) among eyes with diabetic retinopathy (DR). 40 eyes with nonproliferative or proliferative DR without diabetic macular edema received monthly IAI until a DR severity scale (DRSS) level improvement of ≥2 steps was achieved. Eyes were randomized 1:1 to DRSS- or PLI- guided management. At the final 2-year visit, DRSS level was stable or improved compared to baseline in all eyes, and mean PLI decreased by 11% (p = 0.73) and 23.6% (p = 0.25) in the DRSS- and PLI-guided arms. In both arms, the percent of pro re nata (PRN) visits requiring IAI was significantly higher in year 2 versus 1 (p < 0.0001). The percent of PRN visits receiving IAI during year 1 was significantly correlated with the percent of PRN visits with IAI during year 2 (p < 0.0001). Through week 104, 77.4% of instances of DRSS level worsening in the DRSS-guided arm were preceded by or occurred alongside an increase of PLI. Overall, consistent IAI re-treatment interval requirements were observed longitudinally among individual patients. Additionally, PLI increases appeared to precede DRSS level worsening, highlighting PLI as a valuable biomarker in the management of DR.

scholarly journals Hubungan antara tajam penglihatan dengan derajat non-proliferative diabetic retinopathy pada pasien diabetes melitus tipe 2

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Ade John Nursalim ◽  
Vera Sumual
Keyword(s):  
Visual Acuity ◽  
Diabetic Retinopathy ◽  
American Academy ◽  
Disease Severity ◽  
P Value ◽  
Severity Scale ◽  
North Sulawesi ◽  
Snellen Chart ◽  
Disease Severity Scale ◽  
Diabetes Melitus

Abstract: This study aimed to determine the relationship between visual acuity and degree of Non Proliverative Diabetic Retinopathy (NPDR) in patients with type 2 diabetes mellitus (T2DM). This study was conducted in Retina Subdivision Ophthalmology Department Prof. Dr. R. D. Kandou Hospital, Manado, North Sulawesi, Indonesia. Samples were 354 eyes. Visual acuity examination was performed on all patients diagnosed with NPDR by using a Snellen chart at 6 meters distance. NPDR degree was graded according to the International Clinical Diabetic Retinopathy Disease Severity Scale of the American Academy of Ophthalmology. Correlation analysis between visual acuity and the NPDR degree was done by using Kruskal Wallis test which showed a P value of 0.185 (> 0.05). Conclusion: Visual acuity had no significant relationship to the degree of NPDR.Keywords: visual acuity, NPDR, T2DMAbstrak: Penelitian ini bertujuan untuk mengetahui hubungan antara tajam penglihatan dengan derajat Non Proliverative Diabetic Retinopathy (NPDR) pada penyandang diabetes melitus tipe 2 (DMT2). Penelitian ini dilakukan di Poliklinik Mata Subdivisi Retina RSUP Prof. Dr. R. D. Kandou Manado, Provinsi Sulawesi Utara, Indonesia. Sampel penelitian berjumlah 354 mata. Pemeriksaan visus dilakukan pada semua pasien yang terdiagnosis NPDR dengan menggunakan Snellen chart pada jarak 6 meter. Penilaian derajat NPDR berdasarkan International Clinical Diabetic Retinopathy Disease Severity Scale dari American Academy of Ophthalmology. Analisis hubungan visus dengan derajat NPDR dilakukan dengan uji statistik Kruskal Wallis yang menunjukkan nilai P = 0,185 (>0,05). Simpulan: Tidak terdapat hubungan bermakna antara tajam penglihatan (visus) dan derajat NPDR.Kata kunci: visus, NPDR, DMT2

scholarly journals The 2-Year Leakage Index and Quantitative Microaneurysm Results of the RECOVERY Study: Quantitative Ultra-Widefield Findings in Proliferative Diabetic Retinopathy Treated with Intravitreal Aflibercept

2021 ◽  
Vol 11 (11) ◽  
pp. 1126
Author(s):  
Amy S. Babiuch ◽  
Charles C. Wykoff ◽  
Sari Yordi ◽  
Hannah Yu ◽  
Sunil K. Srivastava ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Automatic Segmentation ◽  
Significant Decline ◽  
Severity Scale ◽  
Sustained Reduction ◽  
Intravitreal Aflibercept ◽  
Central Subfield Thickness ◽  
Recovery Study ◽  
To Receive

Eyes with proliferative diabetic retinopathy (PDR) have been shown to improve in the leakage index and microaneurysm (MA) count after intravitreal aflibercept (IAI) treatment. The authors investigated these changes via automatic segmentation on ultra-widefield fluorescein angiography (UWFA). Forty subjects with PDR were randomized to receive either 2 mg IAI every 4 weeks (Arm 1) or every 12 weeks (Arm 2) through Year 1. After Year 1, Arm 1 switched to quarterly IAI and Arm 2 to monthly IAI through Year 2. By Year 2, the Arm 1 leakage index decreased by 43% from Baseline (p = 0.03) but increased by 59% from Year 1 (p = 0.04). Arm 2 decreased by 61% from Baseline (p = 0.008) and by 31% from Year 1 (p = 0.12). Both cohorts exhibited a significant decline in MAs from Baseline to Year 2 (871 to 410; p < 0.001; 776 to 207; p < 0.001, respectively). Subjects with an improved leakage and MA count showed a more significant improvement in the Diabetic Retinopathy Severity Scale (DRSS) score. Moreover, central subfield thickness (CST) was positively associated with changes in the leakage index. In conclusion, the leakage index and MA counts significantly improved from Baseline following IAI treatment, and monthly injections provided a more rapid and sustained reduction in these parameters compared with quarterly injections.

scholarly journals The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Li Lin ◽  
Meng Li ◽  
Yijin Huang ◽  
Pujin Cheng ◽  
Honghui Xia ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Supervised Learning ◽  
Automated Detection ◽  
Training Data ◽  
Classification Algorithms ◽  
Fundus Images ◽  
Severity Scale ◽  
Great Opportunity ◽  
Detection Algorithms

AbstractAutomated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.

scholarly journals Diabetic Retinopathy Improved Detection Using Deep Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 11970
Author(s):  
Angel Ayala ◽  
Tomás Ortiz Figueroa ◽  
Bruno Fernandes ◽  
Francisco Cruz
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Medical Problem ◽  
The Body ◽  
Severity Scale ◽  
Permanent Damage ◽  
Model Training ◽  
Fundus Oculi

Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic retinopathy presence. The latter is probably a blind patient. Our proposal presented an accuracy of 97.78%, allowing for the confident prediction of diabetic retinopathy in fundus oculi images.

Sign in / Sign up

Export Citation Format

Share Document