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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要过去十年中技术和外科手术的进步带来了显着的进步接受救生手术的新生儿数量增加。通常会被分流到新生儿重症监护病房 (NICU) 进行疼痛管理阿片类药物，主要是吗啡、芬太尼和美沙酮，但有大量证据表明。体外、动物和人体研究强烈表明，这种阿片类药物治疗方案会导致严重疼痛对发育中的神经系统造成长期持续且可能永久的创伤性伤害新生儿。我们提出了一种用于早期疼痛检测的新型机器学习和计算机视觉方法 (EPD) 重点关注 NICU 的术后新生儿至少提醒 NICU 护理人员。在疼痛发作前约 30 分钟，EPD 将允许 NICU 工作人员使用速效、节省阿片类药物的药物药物，例如静脉注射扑热息痛、布洛芬或酮咯酸，与非药物联合使用药理学方法，“保持在疼痛之前”，同时避免阿片类药物治疗，耐受性、戒断性及其相关的副作用。我们的 NICU 专家、生物科学家和计算机专家团队将展示以下证据：手术后新生儿可靠 EPD 的概念采用以下目标： 1）收集临床信息和多模态数据（面部表情、身体运动、哭泣频率、生命体征）用于新生儿术后疼痛预测。我们将收集并标记多模态信号（面部表情、身体动作、哭泣坦帕综合医院新生儿重症监护病房 (NICU) 约 60 名手术后疼痛的新生儿的频率、生命体征）我们将把这些数据与另一组约 60 名新生儿的类似数据结合起来。（总共约 120 名新生儿）是 2019 年至 2022 年在 TGH 使用相同的系统和方法收集的。 2) 展示预测新生儿术后疼痛发作的概念验证。从目标 1 的训练案例中收集的多模态数据将为训练卷积神经网络来预测术后新生儿疼痛的发作时间在测试用例中，EPD 的性能目标是疼痛预测 ~ 30。疼痛发生前几分钟，置信概率为 90%，我们的目的是破坏当前的情况。标准[手术；镇静；阿片类药物依赖；耐受；戒断；出院]支持更安全的阿片类药物节约方法[手术；非阿片类药物治疗；我们的第二阶段研究将添加来自更多不同患者群体的数据并进行检查。 EPD 对压力生物标志物的可能影响，例如头发、皮肤、血液中的皮质醇、去甲肾上腺素，对公共健康的主要好处可能是保护最脆弱的人群。患者群体的未来健康和福祉免受不必要的损害。