Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence

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 cont...

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Bibliographic Details
Published in:NPJ digital medicine 2023-03, Vol.6 (1), p.39-39, Article 39
Main Authors: Mosquera-Lopez, Clara, Wilson, Leah M., El Youssef, Joseph, Hilts, Wade, Leitschuh, Joseph, Branigan, Deborah, Gabo, Virginia, Eom, Jae H., Castle, Jessica R., Jacobs, Peter G.
Format: Article
Language:English
Subjects:
631/114/1305
692/163/2743/137/1418
Artificial intelligence
Biomedicine
Biotechnology
Diabetes
Digital technology
Insulin
Meals
Medicine
Medicine & Public Health
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Summary: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
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-023-00783-1