Impact of severity of diabetic retinopathy on quality of life in type 2 Indian diabetic patients

Yadav, Preeti; Singh, Saumyendra Vikram; Nada, Manisha; Dahiya, Monika

doi:10.18203/2394-6040.ijcmph20205694

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…A recent longitudinal and observational study showed that QoL significantly decreases with aggravation of DR severity from mild NPDR to PDR. [ 57 ] Furthermore, a cross-sectional study showed that vision-related functional burden is significantly greater in patients with severe NPDR or PDR versus those with no retinopathy. [ 58 ]…”

Section: Discussionmentioning

confidence: 99%

Update on Management of Non-proliferative Diabetic Retinopathy without Diabetic Macular Edema; Is There a Paradigm Shift?

Arabi

Tadayoni

Ahmadieh

et al. 2022

JOVR

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Diabetic retinopathy (DR) is the major cause of visual impairment and blindness in the working-age population. Conventional management for nonproliferative diabetic retinopathy (NPDR) without diabetic macular edema (DME) is derived from the findings of the Early Treatment Diabetic Retinopathy Study (ETDRS). Although the ETDRS protocol basically includes observation, selected cases of severe NPDR may undergo scatter laser photocoagulation. Post-hoc analysis of recent trials has shown that patients with NPDR receiving intravitreal anti-vascular endothelial growth factor (anti-VEGF) for DME would experience improvement in the DR severity scale (DRSS). In addition, recent randomized trials (PANORAMA and Protocol W) have revealed that early intervention with intravitreal aflibercept in eyes with moderately severe to severe NPDR is associated with significant improvement in DRSS and reduced vision-threatening complications of DR. Based on recent studies, it seems that the therapeutic approach to NPDR may undergo a substantial change and a paradigm shift toward considering early intervention with the administration of intravitreal anti-VEGF injections. However, the long-term results and the duration of adherence to anti-VEGF therapy for eyes with NPDR are not yet defined. It is also not apparent whether improvement in DRSS is a true disease modification. Studies showed that DRSS improvement is not associated with retinal reperfusion. In addition, DRCR.net Protocol W showed no visual acuity benefit with the early intravitreal aflibercept injection in moderate to severe NPDR as compared with performing observation plus intravitreal aflibercept applied only after progression to proliferative DR or vision-impairing DME. The cost–benefit ratio is also a challenge. Herein, we look at different aspects of early anti-VEGF application and discuss its pros and cons in the process of treating NPDR.

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Section: Discussionmentioning

confidence: 99%

Update on Management of Non-proliferative Diabetic Retinopathy without Diabetic Macular Edema; Is There a Paradigm Shift?

Arabi

Tadayoni

Ahmadieh

et al. 2022

JOVR

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show abstract

“…According to statistics [ 4 ], out of the 463 million individuals diagnosed with diabetes worldwide, approximately 30% also experience DR. This disease demonstrates a progressive nature, highlighting the importance of timely diagnosis and treatment to prevent permanent vision loss [ 8 ]. DR can be categorized into two subtypes: nonproliferative DR (NPDR) and proliferative DR (PDR).…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty

Mahapatra²,

Acharya

et al. 2023

Sensors

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Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

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“…According to statistics [4], out of the 463 million individuals diagnosed with diabetes worldwide, approximately 30% also experience DR. This disease demonstrates a progressive nature, highlighting the importance of timely diagnosis and treatment to prevent permanent vision loss [8]. DR can be categorized into two subtypes: non-proliferative DR (NPDR) and proliferative DR (PDR).…”

Section: Introductionmentioning

confidence: 99%

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

2023

IJFMR

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Diabetic retinopathy is a severe problem of long-term diabetes, which affects the human eye and gradually leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classi- fier, and the DenseNet 121 network, for Diabetic retionopathy detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits not balanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The better performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can sig- nificantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

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Impact of severity of diabetic retinopathy on quality of life in type 2 Indian diabetic patients

Cited by 6 publications

References 14 publications

Update on Management of Non-proliferative Diabetic Retinopathy without Diabetic Macular Edema; Is There a Paradigm Shift?

Update on Management of Non-proliferative Diabetic Retinopathy without Diabetic Macular Edema; Is There a Paradigm Shift?

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

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