Early Stage Effectiveness of the Automated Insulin Delivery System-Is Artificial Intelligence Really Effective?

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dc.contributor.authorCetin, Ferhat
dc.contributor.authorGoncuoglu, Enver Sukru
dc.contributor.authorAbali, Saygin
dc.contributor.authorArslanoglu, Ilknur
dc.contributor.authorDeyneli, Oguzhan
dc.contributor.authorCaklili, Ozge Telci
dc.contributor.authorTurna, Hulya Yalin
dc.date.accessioned2025-10-11T20:47:38Z
dc.date.available2025-10-11T20:47:38Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
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 significant just after the learning phase. Methods: The Medtrum A8 TouchCare (R) Nano system was used on 15 patients with type 1 diabetes. Daily continuous glucose monitoring (CGM) data pre-automated insulin delivery (AID) was statistically 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. 76.6-81.7-83.881.5 (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 insignificant by means of the improvement in glycemia (P > .05). No significant change in glycemic parameters between 15, 30, and 90 days were noted (P > .05). Conclusion: Artificial intelligence-augmented AID becomes effective at the very early stages of initiation. 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.en_US
dc.identifier.doi10.5152/erp.2025.24618
dc.identifier.issn2822-6135
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-105003181819en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.trdizinid1343549en_US
dc.identifier.urihttps://doi.org/10.5152/erp.2025.24618
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1343549
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21488
dc.identifier.volume29en_US
dc.identifier.wosWOS:001478732700004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherAvesen_US
dc.relation.ispartofEndocrinology Researchand Practiceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectArtificial pancreasen_US
dc.subjectglycemic controlen_US
dc.subjectautomated insulin deliveryen_US
dc.subjecttype 1 diabetesen_US
dc.titleEarly Stage Effectiveness of the Automated Insulin Delivery System-Is Artificial Intelligence Really Effective?en_US
dc.typeArticleen_US

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