1. Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.
- Author
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Mosquera-Lopez, Clara, Wilson, Leah M., El Youssef, Joseph, Hilts, Wade, Leitschuh, Joseph, Branigan, Deborah, Gabo, Virginia, Eom, Jae H., Castle, Jessica R., and Jacobs, Peter G.
- Subjects
PANCREAS ,CARBOHYDRATE metabolism ,CONFIDENCE intervals ,FOOD consumption ,MACHINE learning ,ARTIFICIAL intelligence ,BLOOD sugar ,INSULIN ,FOOD portions ,RANDOMIZED controlled trials ,PRE-tests & post-tests ,COMPARATIVE studies ,INSULIN pumps ,DESCRIPTIVE statistics ,STATISTICAL sampling ,CROSSOVER trials ,SENSITIVITY & specificity (Statistics) ,DATA analysis software ,MEALS ,ALGORITHMS - Abstract
We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70–180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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