Objective Method for Diagnosis of Chronic Pain

疼痛评估对于提供适当的患者护理和在临床环境下评估其疗效至关重要。准确区分真实和虚假疼痛的能力在确定声称患有慢性疼痛(例如，车祸伤害索赔)的患者的适当补偿水平时也是至关重要的。2018年，加拿大的工作场所和汽车索赔金额为150亿美元，其他国家的金额要高得多(美国每年约为5600亿美元)。我们的行业合作伙伴卡玛斯慢性疼痛医疗诊所(卡玛斯诊所)专门从事慢性非恶性疼痛患者的评估和管理，也是我们这项研究项目的行业合作伙伴。为了评估疼痛，医生通常依靠患者的主观陈述，或不可靠的成像技术，因为检测到的异常并不总是表明疼痛，也不总是出现在慢性疼痛的病例中。在这项研究中，我们将通过创建一种诊断慢性疼痛的客观方法来解决准确检测真实疼痛的问题。我们将创建和评估一个自动的慢性疼痛诊断系统，该系统将输出计算机诊断的疼痛分数，并能够准确区分真实和虚假的疼痛。与人工方法相比，拟议的自动疼痛诊断系统(APDS)将增强患者护理并提高临床实践效率，人工方法在繁忙的临床环境中费力、不准确且难以使用。通过使用最先进的3D技术和人工智能(AI)算法，如深度卷积神经网络(DCNN)，拟议的APDS将在精度和效率上超过人类观察者。

Pain assessment is essential to providing proper patient care and assessing its efficacy under clinical settings. The ability to accurately distinguish real from fake pain is also crucial in the determination of the appropriate level of compensation for a patient that claims to be suffering from chronic pain (e.g., motor accident injury claims). The sums paid out for workplace and motor claims was $15 billion in 2018 in Canada and is substantially higher in other countries (approximately $560 billion dollars each year in USA). Our industry partner, Karmy Chronic Pain Medical Clinics (Karmy Clinics) specializes in assessment and management of patients with chronic non-malignant pain and is our industry partner for this research project. To assess pain, physicians typically rely on patients' statements, which are subjective, or imaging technologies, which are unreliable as detected abnormalities do not always indicate pain and are not always present in cases of chronic pain. In this research, we will address the problem of accurately detecting authentic pain by creating an Objective Method for Diagnosis of Chronic Pain. We will create and evaluate an automated chronic pain diagnostic system that will output a computer diagnosed pain score with the ability to accurately distinguish real from fake pain. The proposed Automatic Pain Diagnosis System (APDS) will enhance patient care and improve clinical practice efficiencies compared to manual approaches, which are laborious, inaccurate and difficult to use in busy clinical settings. The proposed APDS will outperform a human observer in accuracy and efficiency by using the most advanced 3D technologies and Artificial Intelligence (AI) algorithms such as Deep Convolutional Neural Networks (DCNNs).