Automatic Multimodal Assessment of Pain in Dementia

痴呆症疼痛的自动多模式评估

• 批准号: 10288413
• 负责人: Zakia Hammal
• 金额: $ 34.47万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-16 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10288413
• 关键词: Acute Pain; Address; Adult; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Area; Beds; Behavior; Behavioral; Child; Clinical; Code; Collaborations; Collection; Computer Models; Computer Vision Systems; Data; Data Collection; Dementia; Development; Disadvantaged; Elderly; Face; Facial Expression; Funding; Goals; Head Movements; Human; Impairment; Individual; Long-Term Care; Longitudinal cohort; Longitudinal cohort study; Low Back Pain; Machine Learning; Measurement; Measures; Metadata; Methodology; Methods; Modality; Modeling; Morbidity - disease rate; Movement; Nonverbal Communication; Pain; Pain Measurement; Pain intensity; Pain management; Parents; Participant; Patient Care; Patients; Persons; Population; Property; Protocols documentation; Quality of life; Recording of previous events; Research; Research Personnel; Sampling; Skilled Nursing Facilities; Standardization; System; Techniques; Thinness; Time; Training; Video Recording; Work; aging population; base; chronic pain; clinically relevant; data sharing; design; experience; improved; medical specialties; mortality; multimodality; pain model; parent grant

Project Summary/Abstract Chronic and acute pain conditions increase with aging and with age-associated conditions, including Alzheimer's disease and similar dementias, causing suffering, exacerbating diminished quality of life, and likely leading to further morbidity and increased mortality. Understanding and treatment of pain is particularly challenging among populations whose ability to communicate verbally may be impaired, among which patients with Alzheimer's disease and related dementias are of particular importance if only because of the increased aging population. A substantial body of research suggests that methods based on nonverbal indicators of pain yield reliable and valid measurement of pain in patient with dementias. Some of this work has indicated that patients with dementia may in fact be hyper-reactive to pain, rendering the pursuit of improved techniques for assessing pain in the dementia population even more important. Existing techniques for assessing nonverbal indicators of pain have several disadvantages which limit their utility. In particular, they require human observers, are dependent on specialty training, can be laborious, and cannot be applied on a continuous basis. Advances in computer vision and machine learning have the potential to overcome some of these shortcomings. Our “parent proposal” project aims to develop advanced, automated computer-vision and machine-learning models for assessing nonverbal aspects of pain in a longitudinal cohort study of people with low back pain. This supplement proposal seeks to extend our work on the “parent grant”, which aims to “build a fully automatic, multimodal (face, head, and body movement) system to measure the occurrence and intensity of low back pain from video”. In this supplement project, these aims will be extended to older adults with dementia. We will refine the principles discovered for pain assessment in the low back pain population, extend, and evaluate their application in a sample of older adults with dementias in extended-care facilities. Participants, long-term care residents with dementia, were video-recorded during a baseline state as they were lying still on a bed or examination table and then as they underwent a standardized protocol of movements designed to identify painful areas. Participants' face, head, and body movement will be used for the development of automatic measures of the occurrence and intensity of pain. To do so, face, head, and body movement will be automatically tracked using fully- automatic methods. The tracking results will be used to train end-to-end deep- leaning based classifiers to automatically measure the occurrence and intensity of pain in older adults with dementia. To investigate the validity of the proposed classifiers, we will compare automated measurement of pain intensity to reliable and objective pain intensity coding. MANOVA will be used to quantify the relationship between the individual modalities and their combination for the measurement of the occurrence and intensity of pain in older adults with dementia.

项目总结/摘要 慢性和急性疼痛状况随着年龄的增长和与年龄相关的状况而增加，包括阿尔茨海默氏症 疾病和类似的痴呆症，造成痛苦，加剧生活质量下降，并可能导致 发病率和死亡率增加。对疼痛的理解和治疗尤其具有挑战性 在语言交流能力可能受损的人群中， 阿尔茨海默氏病和相关痴呆症是特别重要的，如果只是因为增加的老龄化， 人口大量研究表明，基于非语言疼痛指标的方法 痴呆患者疼痛的可靠和有效的测量。其中一些研究表明， 事实上，痴呆症患者可能对疼痛反应过度，因此寻求改进的评估技术， 疼痛在痴呆症人群中更为重要。评估非语言指标的现有技术 疼痛有几个限制其效用的缺点。特别是，它们需要人类观察者， 依赖于专业训练，可能是费力的，并且不能在连续的基础上应用。进展 计算机视觉和机器学习有可能克服其中一些缺点。我们的“父母 该项目旨在开发先进的自动化计算机视觉和机器学习模型， 在一项对腰痛患者的纵向队列研究中评估疼痛的非语言方面。这种补充剂 一项提案旨在扩大我们在“父母补助金”方面的工作，其目的是“建立一个全自动的、多模式的 (face头部和身体运动）系统来测量来自视频的腰痛的发生和强度”。 在这个补充项目中，这些目标将扩展到老年痴呆症患者。我们将完善 在腰痛人群中发现的疼痛评估原则，扩展并评估其应用 在老年痴呆症患者的样本中。 参与者是患有痴呆症的长期护理居民，他们在基线状态下进行视频记录， 他们静静地躺在床上或检查台上，然后接受标准化的运动方案， 用来识别疼痛部位参与者的面部，头部和身体运动将用于开发 自动测量疼痛的发生和强度。要做到这一点，面部，头部和身体的运动将是 使用全自动方法自动跟踪。跟踪结果将用于训练端到端深度- 基于学习的分类器自动测量老年人疼痛的发生和强度， 痴呆为了研究所提出的分类器的有效性，我们将比较自动测量的 疼痛强度的可靠和客观的疼痛强度编码。MANOVA将用于量化关系 的发生率和强度的测量， 老年痴呆症患者的疼痛。