Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol.

Autor: Andellini M; School of Engineering, University of Warwick, CV4 7AL Coventry, UK., Haleem S; School of Engineering, University of Warwick, CV4 7AL Coventry, UK., Angelini M; HTA Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Ritrovato M; HTA Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Schiaffini R; Diabetes Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Iadanza E; School of Engineering, University of Warwick, CV4 7AL Coventry, UK.; Medical Biotechnologies Department, University of Siena, Siena, Toscana, Italy., Pecchia L; School of Engineering, University of Warwick, CV4 7AL Coventry, UK.
Jazyk: angličtina
Zdroj: Health and technology [Health Technol (Berl)] 2023; Vol. 13 (1), pp. 145-154. Date of Electronic Publication: 2023 Jan 23.
DOI: 10.1007/s12553-022-00719-x
Abstrakt: Purpose: Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device.
Methods: This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.
Results: Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm.
Conclusion: This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions.
Trial Registration: ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.
Supplementary Information: The online version contains supplementary material available at 10.1007/s12553-022-00719-x.
Competing Interests: Competing interestsThe authors declare that they have no competing interests.
(© The Author(s) 2023.)
Databáze: MEDLINE
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