Moscato Serena, Orlandi Silvia, Di Gregorio Francesco, Lullini Giada, Pozzi Stefania, Sabattini Loredana, Chiari Lorenzo, La Porta Fabio
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, Alma Mater Studiorum University of Bologna, Bologna, Italy.
Health Science and Technologies - Interdepartmental Center for Industrial Research (CIRI-SDV), Alma Mater Studiorum University of Bologna, Bologna, Italy.
BMJ Open. 2023 Nov 22;13(11):e073534. doi: 10.1136/bmjopen-2023-073534.
Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors.
We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods.
The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation.
NCT05747040.
数以百万计的人在遭受中枢或周围神经系统损伤后存活下来,需要进行神经康复治疗。除了身体和认知障碍外,许多神经康复患者还会经历疼痛,这种疼痛往往未得到广泛认识且治疗不足。对于多发性硬化症(MS)患者来说尤其如此,疼痛是他们最常见的症状之一。在临床实践中,疼痛评估通常基于主观估计。由于包括情绪或认知方面在内的众多因素的影响,这种方法可能导致评估不准确。迄今为止,尚无客观且易于使用的临床方法能够对疼痛进行客观量化,也无法对两种主要疼痛类型(伤害性疼痛与神经性疼痛)进行诊断区分。可穿戴技术和人工智能(AI)有潜力通过持续监测患者的健康参数并从中提取有意义的信息来填补这一空白。因此,我们提议开发一种新的由人工智能驱动的自动工具,利用可穿戴传感器收集的生理信号来评估神经康复治疗期间的疼痛及其特征。
我们的目标是招募15名正在接受物理治疗的MS患者。在研究过程中,参与者将连续三天佩戴腕带,并在物理治疗前后接受监测。将收集传统使用的疼痛评估问卷和量表(即疼痛DETECT、神经病理性疼痛4问题、欧洲五维健康量表)以及生理信号(光电容积脉搏波描记法、皮肤电活动、皮肤温度、加速度计数据)的测量结果。将识别生理信号的相关参数,并使用人工智能算法开发自动分类方法。
该研究已获得当地伦理委员会(285 - 2022 - SPER - AUSLBO)的批准。参与者需要提供书面知情同意书。研究结果将通过在国际会议和科学期刊上发表进行传播,并且还将纳入博士论文。
NCT05747040。