An AI-based Multimodal Approach to Predict Pain in Postnatal Care Scenarios

基于人工智能的多模式方法来预测产后护理场景中的疼痛

• 批准号: 10546650
• 负责人: PETER Randolph MOUTON
• 金额: $ 31.52万
• 依托单位: STEREOLOGY RESOURCE CENTER, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546650
• 关键词: Absence of pain sensation; Acetaminophen; Address; Admission activity; Adverse effects; Anatomy; Anesthesia procedures; Animals; Behavior; Biological Markers; Biological Sciences; Blood; Caregivers; Clinical; Computer Vision Systems; Computers; Crying; Data; Data Scientist; Detection; Educational workshop; Facial Expression; Fentanyl; Frequencies; Future; General Hospitals; Hair; Health; Health Benefit; Hour; Human; Hydrocortisone; Ibuprofen; In Vitro; Incidence; Industry; Innovation Corps; Intention; Intervention; Interview; Intravenous; Ketorolac; Label; Length of Stay; Life; Machine Learning; Medical; Methadone; Modeling; Monitor; Morphine; Movement; Narcotics; Neonatal; Neonatal Abstinence Syndrome; Neonatal Intensive Care Units; Neurologic; Newborn Infant; Norepinephrine; Operative Surgical Procedures; Opiate Addiction; Opioid; Opioid Analgesics; Pain; Pain Measurement; Pain management; Patients; Performance; Personal Satisfaction; Pharmaceutical Preparations; Phase; Physiology; Postnatal Care; Postoperative Pain; Postoperative Period; Probability; Procedures; Public Health; Regimen; Research; Savings; Sedation procedure; Signal Transduction; Skin; Specialist; Stress; Survival Rate; System; Techniques; Technology; Testing; Time; Training; Trauma; Triage; Urine; Videotape; Withdrawal; Work; base; cohort; convolutional neural network; cost; deep neural network; detection platform; innovative technologies; machine learning algorithm; meter; multimodal data; multimodality; neonatal brain; neonate; non-invasive system; non-opioid analgesic; novel; opioid sparing; opioid use; opioid withdrawal; pain scale; patient population; phase 2 study; side effect

PROJECT SUMMARY Advances in technology and surgical procedures in the past decade have led to a remarkable increase in numbers of newborns subjected to lifesaving surgery. These postoperative neonates are customarily triaged to neonatal intensive care units (NICUs) for pain management with opioids, primarily morphine, fentanyl, and methadone. However, substantial evidence from in- vitro, animal, and human studies strongly suggests this severe pain-to-opioids regimen causes long-lasting and likely permanent traumatic harm to the developing neurological systems of neonates. We propose a novel machine learning and computer vision approach for early pain detection (EPD) with emphasis on postoperative neonates in NICU. By alerting NICU caregivers a minimum of ~ 30 minutes prior to pain onset, EPD will allow NICU staff to use fast-acting, opioid-sparing medications, e.g., intravenous paracetamol, ibuprofen, or ketorolac, in conjunction with non- pharmacological approaches, to “stay ahead of the pain” while avoiding opioid treatments, tolerance, withdrawal, and their associated side effects. Our team of NICU specialists, bioscientists, and computer experts will demonstrate proof-of- concept for reliable EPD in post-surgical neonates using the following aims: 1) Collect clinical information and multimodal data (facial expression, body movement, crying frequency, vital signs) for post-surgical pain prediction in neonates. We will collect and label multi-modal signals (facial expression, body movements, crying frequency, vital signs) from ~ 60 neonates in post-surgical pain at the NICU at Tampa General Hospital (TGH). We will combine these data with similar data from another cohort of ~60 neonates (total ~ 120 neonates) collected using the same system and approach at TGH from 2019 to 2022. 2) Show proof-of-concept for predicting the onset of post-surgical pain in neonates. The multimodal data collected from the training cases in Aim 1 will provide the ground truth for training a convolutional neural network to predict time-to-onset of pain for postoperative neonates in the testing cases. The performance target for the EPD in the test cases is pain prediction ~ 30 minutes prior to pain onset with a 90% confidence probability. Our intention is to disrupt the current standard [surgery; sedation; postoperative pain; opioid dependence, tolerance, withdrawal; discharge] in favor of a safer opioid-sparing approach [surgery; sedation; non-opioid treatment; discharge]. Our Phase 2 studies will add data from more diverse patient populations and examine the possible effects of EPD on stress biomarkers, e.g., cortisol, norepinephrine in hair, skin, blood, or urine. The major benefit to public health will be protection of perhaps the most vulnerable patient populations from unnecessary damage to their future health and well-being.

项目摘要 在过去十年中，技术和外科手术的进步导致了非凡的 接受救生手术的新生儿数量增加。这些术后新生儿 通常被与新生儿重症监护病房（NICUS）进行分类，用于疼痛管理 o型蛋白，原代吗啡，芬太尼和方法adone。但是，来自内在的大量证据 体外，动物和人类研究强烈表明这种严重的疼痛与阿片类药物是原因 对发展的神经系统的持久且可能永久性的创伤性伤害 新生儿。 我们提出了一种新颖的机器学习和计算机视觉方法，以进行早期疼痛检测 （EPD）重点是NICU术后新生儿。通过向NICU护理人员提醒至少 疼痛发作前约30分钟，EPD将允许NICU员工使用快速效果，分类 药物，例如静脉注射乙酰氨基酚，布洛芬或酮洛克拉克，与非 - 药理学方法，“保持疼痛领先”，同时避免阿片类药物治疗， 耐受性，戒断及其相关的副作用。 我们的NICU专家，生物科学家和计算机专家团队将展示证明 使用以下目的，可靠的术后新生儿EPD的概念： 1）收集临床信息和多模式数据（面部表达，身体运动， 哭泣的频率，生命体征）用于新生儿后手术后疼痛预测。 我们将收集和标记多模式信号（面部表情，身体运动，哭泣 频率，生命体征。 医院（TGH）。我们将将这些数据与来自另一个约60个新生儿队的同类数据相结合 （总计约120名新生儿）从2019年至2022年使用相同的系统和方法收集。 2）显示了预测新生儿后手术后疼痛发作的概念证明。 AIM 1中从培训案例中收集的多模式数据将为地面真相 培训卷积神经网络以预测术后新生儿的疼痛时间 在测试案例中。测试案例中EPD的性能目标是疼痛预测〜30 疼痛开始前几分钟，置信度概率为90％。我们的目的是破坏当前 标准[手术；镇静术后疼痛；阿片类药物依赖，耐受性，戒断； ]支持更安全的阿片类药物方法[手术；镇静非阿片类药物治疗； 释放]。我们的第二阶段研究将添加来自更多潜水员的患者人群的数据并检查 EPD对压力生物标志物的可能影响，例如皮质醇，去皮质肾上腺素，皮肤，皮肤，血液， 或尿液。公共卫生的主要好处将是保护最脆弱的 患者人口受到对未来健康和福祉的不必要损害。