Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.
NPJ Digit Med
; 6(1): 39, 2023 Mar 13.
Article
em En
| MEDLINE
| ID: mdl-36914699
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
NPJ Digit Med
Ano de publicação:
2023
Tipo de documento:
Article