通过心电图对儿科人群进行无创血糖事件检测的人工智能：研究方案
Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol.
作者信息
Andellini Martina, Haleem Salman, Angelini Massimiliano, Ritrovato Matteo, Schiaffini Riccardo, Iadanza Ernesto, Pecchia Leandro
机构信息
School of Engineering, University of Warwick, CV4 7AL Coventry, UK.
HTA Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
出版信息
Health Technol (Berl). 2023;13(1):145-154. doi: 10.1007/s12553-022-00719-x. Epub 2023 Jan 23.
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.
目的
由于血糖控制不佳，1型糖尿病（T1D）儿科患者发生严重低血糖和高血糖事件的风险更高。为降低不良事件风险，患者需要通过手指采血频繁监测血糖或使用连续血糖监测（CGM）系统来实现尽可能最佳的血糖管理。然而，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，该算法使用通过非侵入性设备收集的心电图（ECG）信号来检测血糖事件。
方法
本研究将招募已经在使用CGM的T1D儿科参与者。参与者将佩戴一个额外的非侵入性可穿戴设备，用于记录生理数据和呼吸频率。通过ECG变量驱动的血糖测量是主要结果。收集到的数据将用于基于深度学习（DL）AI算法设计、开发和验证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ECG心跳自动检测低血糖事件。
结果
预计数据收集大约在2023年6月完成。预计将收集到足够的数据来开发和验证AI算法。
结论
这是一项验证研究，将对更大的糖尿病样本人群进行额外测试，以验证之前基于四名健康成年人的初步结果，为AI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标识符：NCT03936634。于2022年3月11日注册，追溯注册，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
在线版本包含可在10.1007/s12553-022-00719-x获取的补充材料。
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