Using Artificial Intelligence to optimize agreement between interstitial sensors and capillary puncture in glycemic assessment and classification

Abstract Introduction Blood glucose monitoring is essential for the management of diabetes mellitus. Continuous interstitial glucose (IG) monitoring systems are less invasive than capillary blood glucose (BG) measurements, but their agreement decreases at higher glucose levels. Artificial intelligence (AI) approaches, particularly recurrent neural networks such as long short-term memory (LSTM), have shown potential to model temporal glucose dynamics and correct inter-method discrepancies. Objective: To develop and validate an AI-based model capable of predicting capillary BG values from IG data, improving agreement between methods and enhancing glycemic status classification. Methods: This retrospective observational study analyzed 708 paired BG-IG measurements obtained from published anonymized datasets. Data preprocessing included Kalman filtering, robust normalization, temporal windowing, and class balancing via oversampling. An LSTM model with dual output was trained to perform both capillary glucose regression and glycemic status classification. Model performance was assessed using regression metrics (MAE, RMSE, R 2 ), classification metrics (accuracy, F1-score), and agreement analysis (Bland-Altman). Results: The AI model substantially reduced the mean bias from +16.27 mg/dL to -2.08 mg/dL and achieved markedly narrower limits of agreement compared with raw BG-IG differences (-129.5 to +162.0 mg/dL vs. -47.3 to +43.2 mg/dL). Glycemic classification accuracy was high for hyperglycemia (94.6%), prediabetes (93.7%) and normoglycemia (100%), with lower performance observed for hypoglycemia (66.7%). Conclusion: LSTM-based AI modeling demonstrated strong capability to predict capillary BG from IG measurements and to correct inter-method discordance. These findings support the potential integration of AI-enhanced glucose estimation into clinical monitoring systems to improve therapeutic decision-making.