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

IF 3.1 Q2 MEDICAL INFORMATICS Health and Technology Pub Date : 2023-01-01 Epub Date: 2023-01-23 DOI:10.1007/s12553-022-00719-x
Martina Andellini, Salman Haleem, Massimiliano Angelini, Matteo Ritrovato, Riccardo Schiaffini, Ernesto Iadanza, Leandro Pecchia
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Abstract

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.

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在儿科人群中通过心电图进行无创血糖事件检测的人工智能:研究方案。
目的:由于血糖控制不佳,儿童 1 型糖尿病 (T1D) 患者发生严重低血糖和高血糖事件的风险更大。为了降低不良事件的风险,患者需要通过频繁使用手指点刺或连续血糖监测系统(CGM)进行血糖监测,以尽可能实现最佳的血糖管理。然而,目前已经提出了几种非侵入性技术,旨在利用基于血糖水平的生理参数变化。本研究的总体目标是验证一种基于人工智能(AI)的算法,利用通过无创设备收集的心电图信号检测血糖事件:本研究将招募已经使用 CGM 的 T1D 儿科参与者。参与者将佩戴额外的无创可穿戴设备,用于记录生理数据和呼吸频率。主要结果是通过心电图变量进行血糖测量。收集到的数据将用于设计、开发和验证基于深度学习(DL)人工智能算法的个性化和通用分类器,该算法能够通过使用可穿戴设备记录的少量心电图自动检测低血糖事件:数据收集工作预计将于 2023 年 6 月左右完成。预计将收集到足够的数据来开发和验证人工智能算法:这是一项验证研究,将在更大的糖尿病样本人群中进行更多测试,以验证之前基于四名健康成人的试点结果,为人工智能算法在自由生活条件下检测儿科糖尿病患者血糖事件的可靠性提供证据:试验注册:ClinicalTrials.gov identifier:NCT03936634。注册日期:2022 年 3 月 11 日,回顾性注册,https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.补充信息:在线版本包含补充材料,可查阅 10.1007/s12553-022-00719-x。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Health and Technology
Health and Technology MEDICAL INFORMATICS-
CiteScore
7.10
自引率
0.00%
发文量
83
期刊介绍: Health and Technology is the first truly cross-disciplinary journal on issues related to health technologies addressing all professions relating to health, care and health technology.The journal constitutes an information platform connecting medical technology and informatics with the needs of care, health care professionals and patients. Thus, medical physicists and biomedical/clinical engineers are encouraged to write articles not only for their colleagues, but directed to all other groups of readers as well, and vice versa.By its nature, the journal presents and discusses hot subjects including but not limited to patient safety, patient empowerment, disease surveillance and management, e-health and issues concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. The journal also addresses the medical, financial, social, educational and safety aspects of health technologies as well as health technology assessment and management, including issues such security, efficacy, cost in comparison to the benefit, as well as social, legal and ethical implications.This journal is a communicative source for the health work force (physicians, nurses, medical physicists, clinical engineers, biomedical engineers, hospital engineers, etc.), the ministries of health, hospital management, self-employed doctors, health care providers and regulatory agencies, the medical technology industry, patients'' associations, universities (biomedical and clinical engineering, medical physics, medical informatics, biology, medicine and public health as well as health economics programs), research institutes and professional, scientific and technical organizations.Health and Technology is jointly published by Springer and the IUPESM (International Union for Physical and Engineering Sciences in Medicine) in cooperation with the World Health Organization.
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