通过使用人工智能进行进餐检测和份量估计来实现完全自动化胰岛素输送。
Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.
作者信息
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
机构信息
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA.
出版信息
NPJ Digit Med. 2023 Mar 13;6(1):39. doi: 10.1038/s41746-023-00783-1.
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.
我们展示了一种强大的胰岛素输送系统，该系统包括使用机器学习进行餐时胰岛素剂量计算的自动进餐检测和碳水化合物含量估计，称为强大人工胰腺（RAP）。我们进行了一项随机、单中心交叉试验，以比较使用混合模型预测控制（MPC）算法和RAP系统在未宣布进餐之后的四小时内的餐后血糖控制情况。RAP系统包括一个神经网络模型，用于自动检测进餐并提供推荐的餐时胰岛素剂量。进餐检测算法的灵敏度为83.3%，错误发现率为16.6%，平均检测时间为25.9分钟。虽然葡萄糖曲线下增量面积没有显著差异，但与MPC相比，RAP显著降低了高于范围的时间（血糖>180mg/dL）10.8%（P = 0.04），并且有使处于范围（70 - 180mg/dL）的时间增加9.1%的趋势。RAP和MPC之间低于范围的时间（血糖<70mg/dL）没有显著差异。
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