Clinical Report: Withdrawal of Article on Diabetic Retinopathy Classification

Overview

The article on enhanced classification of diabetic retinopathy images using deep learning has been retracted due to significant overlap with previously published works. The retraction highlights the importance of originality in scientific publications.

Background

Diabetic retinopathy (DR) is a leading cause of vision impairment among diabetic patients, making accurate classification and timely intervention crucial. The use of deep learning techniques for image classification has gained traction, promising improved diagnostic accuracy. However, issues of redundancy in publication can undermine the integrity of scientific literature and patient care.

Data Highlights

No numerical data is available due to the retraction of the article.

Key Findings

• The article was retracted at the request of the Journal Editor and Sage due to redundant publication.
• Significant unreferenced overlap was identified with other publications by the same authors.
• The authors did not respond within the designated timeframe regarding the retraction.
• Retractions emphasize the need for originality and integrity in scientific research.
• Duplicate publications can mislead clinical practice and research advancements.

Clinical Implications

Healthcare professionals should remain vigilant about the originality of research findings to ensure the reliability of clinical guidelines. Awareness of retracted articles is essential to avoid reliance on potentially flawed data in patient care.

Conclusion

The retraction of this article underscores the critical importance of maintaining high standards of originality in scientific publications to support effective clinical practice.

Related Resources & Content

1. Alwakid G, Gouda W, Humayun M, Jhanjhi NZ, Digital Health, 2023 -- Deep learning-enhanced diabetic retinopathy image classification
2. Alwakid G, Gouda W, Humayun M, Diagnostics, 2023 -- Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning
3. Alwakid G, Gouda W, Humayun M, Jhanjhi N, Digital Health, 2023 -- Enhancing diabetic retinopathy classification using deep learning
4. DIGITAL HEALTH — Retraction: Enhancing diabetic retinopathy classification using deep learning
5. Retinal Physician — Deep Learning to Detect Diabetic Retinopathy: Understanding the Implications
6. Frontiers in Medicine — Detection of Referable Diabetic Retinopathy using Machine Learning on Routine Clinical Data
7. Retinal Physician — Teleretinal Imaging for Diabetic Patients
8. Retraction: Enhancing diabetic retinopathy classification using deep learning
9. Deep Learning to Detect Diabetic Retinopathy: Understanding the Implications
10. Detection of Referable Diabetic Retinopathy using Machine Learning on Routine Clinical Data
11. 12. Retinopathy, Neuropathy, and Foot Care: Standards of Care in Diabetes—2026
12. Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections | npj Digital Medicine
13. Aflibercept, Bevacizumab, or Ranibizumab for Diabetic Macular Edema | New England Journal of Medicine