Early stage effectiveness of the automated insulin delivery system—is artificial intelligence really effective?

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dc.authorid0000-0003-0416-8639
dc.authorid0009-0003-4556-2947
dc.authorid0000-0001-6552-2801
dc.authorid0000-0002-4975-9718
dc.authorid0000-0001-6582-7031
dc.authorid0000-0001-7566-5427
dc.authorid0009-0002-4138-9334
dc.authorid0000-0002-1950-2727
dc.authorid0009-0007-3916-8563
dc.authorid0000-0002-6130-947X
dc.contributor.authorÇetin, Ferhat
dc.contributor.authorGöncüoğlu, Enver Şükrü
dc.contributor.authorAbalı, Saygın
dc.contributor.authorArslanoğlu, İlknur
dc.contributor.authorDeyneli, Oğuzhan
dc.contributor.authorTelci Çaklılı, Özge
dc.contributor.authorYalın Turna, Hülya
dc.contributor.authorŞahiner, Elif
dc.contributor.authorGüzel, Dila
dc.contributor.authorYılmaz, Mehmet Temel
dc.date.accessioned2025-05-07T13:01:24Z
dc.date.available2025-05-07T13:01:24Z
dc.date.issued2025
dc.departmentFakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalı
dc.description.abstractObjective: This study aimed to evaluate the effectiveness of the self-learning capabilities of artificial intelligence (AI) algorithms. The hypothesis was that if the success of closed-loop insulin delivery is mainly attributed to AI algorithms, then the improvement in glycemic control would be more signifi cant just after the “learning” phase. Methods: The Medtrum A8 TouchCare® Nano system was used on 15 patients with type 1 diabetes. Daily continuous glucose monitoring (CGM) data pre-automated insulin delivery (AID) was statisti cally compared with the post-AID period. Results: Patients (median age 32 (6-54) years, 40% female) had a median HbA1c of 8.4% (5.3-10.7) before initiation of AID and a median GMI of 6.6% (5.8-8.3) after 2 weeks. The shifts in glycemia and glycemic variability between the 5-day period pre-AID vs. the first day and the 3 5-day periods post-AID were significant (pre-AID vs. 1-5-10-15 days; time in range (TIR, %): 55.9 vs. 76.6-81.7-83.8- 81.5 (P=.001); Q1 (mg/dL): 123 vs. 112-108-106-110 (P=.009); Q3 (mg/dL): 204 vs. 176-173-168-169 (P=.004); inter-quarter range (IQR, mg/dL): 78 vs. 57.2-56.6-53-55 (P=.002)). The biggest shift in TIR was achieved in the first day (10.1%). Comparative analysis of the 5-day intervals post-AID was insig nificant by means of the improvement in glycemia (P > .05). No significant change in glycemic param eters between 15, 30, and 90 days were noted (P > .05). Conclusion: Artificial intelligence-augmented AID becomes effective at the very early stages of initia tion. There is a need for further research into glycemic changes in the early days of AID initiation to better define the principles of initiating AID systems.
dc.identifier.citationÇetin, F., Göncüoğlu, E. Ş., Abalı, S., Arslanoğlu, İ., Deyneli, O., Telci Çaklılı, Ö., Yalın Turna, H., Şahiner, E., Güzel, D., & Yılmaz, M. T. (2025). Early stage effectiveness of the automated insulin delivery system—is artificial intelligence really effective?. Endocrinology Research and Practice, 29(2), pp. 101-106. https://doi.org/10.5152/erp.2025.24618
dc.identifier.doi10.5152/erp.2025.24618
dc.identifier.endpage106
dc.identifier.issn2822-6135
dc.identifier.issue2
dc.identifier.scopus2-s2.0-105003181819
dc.identifier.scopusqualityQ4
dc.identifier.startpage101
dc.identifier.urihttps://doi.org/10.5152/erp.2025.24618
dc.identifier.urihttps://hdl.handle.net/20.500.13055/978
dc.identifier.volume29
dc.institutionauthorÇetin, Ferhat
dc.institutionauthorid0000-0003-0416-8639
dc.language.isoen
dc.publisherAVES
dc.relation.ispartofEndocrinology Research and Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Pancreas
dc.subjectGlycemic Control
dc.subjectAutomated Insulin Delivery
dc.subjectType 1 Diabetes
dc.titleEarly stage effectiveness of the automated insulin delivery system—is artificial intelligence really effective?
dc.typeArticle
dspace.entity.typePublication

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