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Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes : protocol for data collection under controlled and free-living conditions
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Cisuelo, O., Stokes, K., Oronti, I. B. , Haleem, M. S., Barber, Thomas M., Weickert, Martin O., Pecchia, Leandro and Hattersley, John G. (2023) Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes : protocol for data collection under controlled and free-living conditions. BMJ Open, 13 . e067899. doi:10.1136/bmjopen-2022-067899 ISSN 2044-6055.
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WRAP-development-artificial-intelligence-system-identify-hypoglycaemia-via-ECG-adults-type-1-diabetes-2023.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (350Kb) | Preview |
Official URL: https://doi.org/10.1136/bmjopen-2022-067899
Abstract
Introduction: Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.
Methods and analysis: This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.
Ethics and dissemination: This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.
Trial registration number: NCT05461144.
Item Type: | Journal Article | ||||||||||||
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Subjects: | R Medicine > R Medicine (General) R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine > Metabolic and Vascular Health (- until July 2016) Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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Library of Congress Subject Headings (LCSH): | Diabetes -- Treatment, Insulin, Artificial intelligence -- Medical applications, Medical Informatics, Insulin -- Therapeutic use, Diabetes -- Complications, Hypoglycemia | ||||||||||||
Journal or Publication Title: | BMJ Open | ||||||||||||
Publisher: | BMJ | ||||||||||||
ISSN: | 2044-6055 | ||||||||||||
Official Date: | 18 April 2023 | ||||||||||||
Dates: |
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Volume: | 13 | ||||||||||||
Article Number: | e067899 | ||||||||||||
DOI: | 10.1136/bmjopen-2022-067899 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 2 May 2023 | ||||||||||||
Date of first compliant Open Access: | 2 May 2023 | ||||||||||||
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