Endocrinology and Metabolism

Review Article

Diabetes, obesity and metabolism
Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future: Mingui Kong, Su Jeong Song; Endocrinol Metab. 2024;39(3):416-424. Published online June 10, 2024; DOI: https://doi.org/10.3803/EnM.2023.1913

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Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields. In addition to achieving high precision comparable to that of ophthalmologists, AI-based diagnosis of DR has the potential to improve medical accessibility, especially through telemedicine. In this review paper, we aim to examine the current role of AI in the diagnosis of DR and explore future directions.

Citations

Citations to this article as recorded by

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Original Article

Diabetes, obesity and metabolism
Effects of an Electronic Medical Records-Linked Diabetes Self-Management System on Treatment Targets in Real Clinical Practice: Retrospective, Observational Cohort Study: So Jung Yang, Sun-Young Lim, Yoon Hee Choi, Jin Hee Lee, Kun-Ho Yoon; Endocrinol Metab. 2024;39(2):364-374. Published online March 21, 2024; DOI: https://doi.org/10.3803/EnM.2023.1878; Correction in: Endocrinol Metab 2024;39(3):537

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Abstract PDF Supplementary Material PubReader ePub

Background
This study evaluated the effects of a mobile diabetes management program called “iCareD” (College of Medicine, The Catholic University of Korea) which was integrated into the hospital’s electronic medical records system to minimize the workload of the healthcare team in the real clinical practice setting.
Methods
In this retrospective observational study, we recruited 308 patients. We categorized these patients based on their compliance regarding their use of the iCareD program at home; compliance was determined through self-monitored blood glucose inputs and message subscription rates. We analyzed changes in the ABC (hemoglobin A1c, blood pressure, and low-density lipoprotein cholesterol) levels from the baseline to 12 months thereafter, based on the patients’ iCareD usage patterns.
Results
The patients comprised 92 (30%) non-users, 170 (55%) poor-compliance users, and 46 (15%) good-compliance users; the ABC target achievement rate showed prominent changes in good-compliance groups from baseline to 12 months (10.9% vs. 23.9%, P<0.05), whereas no significant changes were observed for poor-compliance users and non-users (13.5% vs. 18.8%, P=0.106; 20.7% vs. 14.1%, P=0.201; respectively).
Conclusion
Implementing the iCareD can improve the ABC levels of patients with diabetes with minimal efforts of the healthcare team in real clinical settings. However, the improvement of patients’ compliance concerning the use of the system without the vigorous intervention of the healthcare team needs to be solved in the future.

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Stakeholder Perspectives on Implementing DiabeText: Exploring Barriers and Facilitators for a Personalized Diabetes Self-Management SMS Intervention in Spain
Elena Gervilla-García, Patricia García-Pazo, Mireia Guillén-Solà, Federico Leguizamo, Ignacio Ricci-Cabello, María Jesús Serrano-Ripoll, Miquel Bennasar-Veny, Maria Antònia Fiol-deRoque, Escarlata Angullo-Martínez, Rocío Zamanillo-Campos
Diabetology.2026; 7(1): 17. CrossRef

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