Objective Method for Diagnosis of Chronic Pain

慢性疼痛诊断的客观方法

• 批准号: 555388-2020
• 负责人: Mowat, Vicki
• 金额: $ 5.45万
• 依托单位: Sheridan Institute of Technology and Advanced Learning
• 依托单位国家: 加拿大
• 项目类别: Applied Research and Development Grants - Level 2
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722670
• 关键词: Objective; Method; Diagnosis; Chronic; Pain

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).

疼痛评估对于提供适当的患者护理并评估其在临床环境下的疗效至关重要。准确区分真痛和假痛的能力对于确定声称患有慢性疼痛（例如车祸伤害索赔）的患者的适当赔偿水平也至关重要。 2018 年，加拿大为工作场所和汽车索赔支付的金额为 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 Karmy 慢性疼痛医疗诊所（Karmy Clinics）专门从事慢性非恶性疼痛患者的评估和管理，也是我们该研究项目的行业合作伙伴。 为了评估疼痛，​​医生通常依赖患者的主观陈述或成像技术，这些技术不可靠，因为检测到的异常并不总是表明疼痛，并且在慢性疼痛的情况下并不总是存在。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。我们将创建并评估一个自动化慢性疼痛诊断系统，该系统将输出计算机诊断的疼痛评分，能够准确区分真痛和假痛。与手动方法相比，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，手动方法费力、不准确且难以在繁忙的临床环境中使用。 通过使用最先进的 3D 技术和深度卷积神经网络 (DCNN) 等人工智能 (AI) 算法，所提出的 APDS 将在准确性和效率方面超越人类观察者。