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Mosquera-Lopez C, Wilson LM, El Youssef J, Hilts W, Leitschuh J, Branigan D, Gabo V, Eom JH, Castle JR, Jacobs PG. Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence. NPJ Digit Med 2023; 6:39. [PMID: 36914699 PMCID: PMC10011368 DOI: 10.1038/s41746-023-00783-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
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.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph Leitschuh
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jae H Eom
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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