SBIR Phase I: Measuring and Mapping Pain Intensity onto a 3D Image of the Human Body

这项小企业创新研究（SBIR）第一阶段项目的广泛影响/商业潜力将显著改善临床医生和研究人员测量、研究和治疗疼痛的方式。慢性疼痛影响着20%的美国成年人，每年造成的经济损失高达6000亿美元。阿片类药物通常是提供给慢性疼痛患者的第一种治疗选择。目前的疼痛评估采用简单、主观和广义的视觉/语言量表（1-10），几乎无法定位疼痛的来源。对于临床医生和医学研究人员来说，有校准和准确的疼痛测量来帮助患者确定潜在疾病并指导患者进行最有效的治疗是至关重要的。该项目将开发一种新的生物医学工具来量化多模式治疗计划的疼痛数据，以潜在地减少阿片类药物的使用。该工具将改善慢性疼痛患者的生活质量，并帮助阿片类药物患者找到更好的治疗方法。此外，它将帮助具有不同培训水平和经验的临床医生达到最高水平的考试技能。这个小企业创新研究（SBIR）第一阶段项目将通过多个生物传感器捕捉触诊位置、压力和患者疼痛表达，从而将身体检查数字化，从而在人体的3D图像上创建个性化的疼痛强度“轮廓”图。这个全面和校准的数据集可以用来帮助临床医生制定一个更准确的鉴别诊断的潜在的疼痛状况。该项目将推进硬件和软件组件，以便未来与增强现实和人工智能模块集成。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will significantly improve the way clinicians and researchers measure, study and treat pain. Chronic pain affects 20% of U.S. adults and costs $600 billion annually. Opioids are often the first treatment option offered to patients with chronic pain. Currently pain is assessed using simplistic, subjective and generalized visual/verbal scales (1-10) with little ability to localize the origin. It is essential for clinicians and medical researchers to have calibrated and accurate measures of pain to assist patients in determining the underlying disease and directing patients to the most effective therapies. This project will develop a new biomedical tool to quantify pain data for multimodal treatment plans to potentially minimize opioid use. This tool will improve quality of life for people suffering from chronic pain and help those on opioids find better therapies. Furthermore, it will help clinicians with varied levels of training and experience achieve the highest level of exam skill.This Small Business Innovation Research (SBIR) Phase I project will digitize a physical exam by capturing palpation location and pressure and patient pain expression via multiple biosensors to create a personalized “contour” map of pain intensity onto a 3D image of the human body. This comprehensive and calibrated data set can then be used to assist clinicians in developing a more accurate differential diagnosis for the underlying pain condition. This project will advance the hardware and software components for future integration with augmented reality and artificial intelligence modules.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.