He Jian, Luo Anhua, Yu Jiajia, Qian Chengxi, Liu Dongwei, Hou Meijin, Ma Ye
Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China.
School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China.
Front Neurol. 2023 Jul 5;14:1121323. doi: 10.3389/fneur.2023.1121323. eCollection 2023.
Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.
痉挛是一种复杂的神经系统疾病，会导致严重的身体残疾，并影响患者的独立性和日常生活质量。当前的痉挛评估方法因其测量方案不标准化、可靠性有限以及在上运动神经元损伤中区分神经元或非神经元因素的能力不足而受到质疑。随着生物传感器、机器人技术、医学成像、生物力学、远程医疗和人工智能等技术的发展，人们开发了一系列新方法来提高当前临床使用的痉挛评估方法的有效性。我们研究了2016年至今发表的、有可能用于临床环境的当前临床使用的痉挛测量方法以及新开发方法的可靠性和有效性。新的痉挛量表利用扭矩或回声强度等量化信息、速度依赖性特征以及患者自我报告的信息进行半定量分级，与以前的痉挛量表相比具有竞争力或更好的可靠性。医学成像技术，包括近红外光谱、磁共振成像、超声和热成像，可以测量肌肉的血流动力学和代谢、肌肉组织特性或组织温度。基于医学成像的方法在评估和监测肌肉痉挛方面提供定量信息是可行的。便携式设备、基于机器人的设备或肌动测量法，利用来自角度、惯性、扭矩或表面肌电图传感器的信息，借助机器学习算法可以量化痉挛。然而，使用这些设备进行的痉挛测量通常不符合生理规律。重复经颅磁刺激可以评估严重痉挛且失去自主收缩能力的患者。神经肌肉骨骼建模可以评估神经和非神经特性，并可能深入了解痉挛肌肉的潜在病理。远程医疗技术能够实现门诊痉挛评估。新开发的痉挛评估方法旨在使实验方案和结果测量标准化，并实现量化、准确和智能的评估。然而，仍需要更多工作来研究和提高痉挛评估的有效性和准确性。
