O'Muircheartaigh Jonathan, Marquand Andre, Hodkinson Duncan J, Krause Kristina, Khawaja Nadine, Renton Tara F, Huggins John P, Vennart William, Williams Steven C R, Howard Matthew A
Department of Neuroimaging, Institute of Psychiatry, Centre for Neuroimaging Sciences, King's College London, London, United Kingdom.
Hum Brain Mapp. 2015 Feb;36(2):633-42. doi: 10.1002/hbm.22652. Epub 2014 Oct 12.
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. We combined these acquisition and analysis methodologies in a well-characterized postsurgical pain model. The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification. Twenty male volunteers, requiring bilateral, lower jaw third molar extraction (TME), underwent ASL examination prior to and following individual left and right TME, representing presurgical and postsurgical states, respectively. Six ASL time points were acquired at each exam. Each ASL image was preceded by visual analogue scale assessments of alertness and subjective pain experiences. Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting.
近期关于多变量机器学习（ML）技术的报告突出了其在检测疼痛预后和诊断标志物方面的潜在用途。然而，迄今为止的应用主要集中在急性实验性伤害性刺激上，而非临床相关的疼痛状态。这些报告与其他描述动脉自旋标记（ASL）应用于检测持续临床疼痛患者局部脑血流（rCBF）变化的报告同时出现。我们在一个特征明确的术后疼痛模型中结合了这些采集和分析方法。主要目的是：（1）评估手术干预前后获取的rCBF指数的分类准确性；（2）优化维持准确分类所需的数据量。20名需要双侧下颌第三磨牙拔除（TME）的男性志愿者，在分别进行左右TME之前和之后接受了ASL检查，分别代表术前和术后状态。每次检查采集6个ASL时间点。每个ASL图像之前都进行了警觉性和主观疼痛体验的视觉模拟量表评估。使用所有检查的所有数据，一个独立的高斯过程二元分类器成功区分术后和术前状态，准确率为94.73%；使用一半的数据（相当于15分钟扫描时间）可达到80%以上的准确率。这项工作证明了在个体水平上对临床相关疼痛进行高效、概率性预测的概念和可行性。我们讨论了ML技术在寻找诊断、管理和治疗新方法以补充传统患者自我报告方面的潜力。
