Journal Article

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

Castle, Jessica R, Jacobs, Peter G, Mosquera-Lopez, Clara, Gabo, Virginia, Wilson, Leah M, Eom, Jae H, Leitschuh, Joseph, El Youssef, Joseph, Hilts, Wade, Branigan, Deborah

2023

Overview

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.

• Publisher: Nature Publishing Group
• Subject: Artificial intelligence / Diabetes / Digital technology / Insulin / Meals