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

项目总结 在过去的十年里，技术和外科手术的进步带来了引人注目的 接受救命手术的新生儿数量增加。这些手术后的新生儿 通常被分流到新生儿重症监护病房(NICU)进行疼痛控制 阿片类药物，主要是吗啡、芬太尼和美沙酮。然而，来自in-in的大量证据 体外、动物和人体研究强烈表明，这种对阿片类药物的严重疼痛疗法会导致 对发育中的神经系统的持久和可能永久的创伤性伤害 新生儿。 我们提出了一种新的机器学习和计算机视觉方法用于疼痛的早期检测 (EPD)，重点是NICU的术后新生儿。通过提醒NICU照顾者至少 在疼痛发作前约30分钟，EPD将允许NICU工作人员使用快速、非阿片类药物 药物，如静脉注射扑热息痛、布洛芬或酮咯酸，与非 药理学方法，“领先于疼痛”，同时避免阿片类药物治疗， 耐受性、戒断及其相关副作用。 我们由NICU专家、生物科学家和计算机专家组成的团队将展示 手术后新生儿可靠的EPD的概念，其目的如下： 1)收集临床信息和多模式数据(面部表情、身体运动、 哭闹频率、生命体征)预测新生儿手术后疼痛。 我们将收集和标记多模式信号(面部表情、身体动作、哭泣 坦帕综合医院NICU约60名新生儿术后疼痛的频率、生命体征) 医院(TGH)。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 2019年至2022年在TGH使用相同系统和方法收集的(总计~120名新生儿)。 2)对新生儿术后疼痛的预测具有概念性。 从目标1中的培训案例中收集的多模式数据将为 训练卷积神经网络预测新生儿术后疼痛发生的时间 在测试案例中。在测试案例中，环保署的表现目标是疼痛预测~30 在疼痛发作前几分钟，有90%的可信概率。我们的意图是扰乱海流 标准[手术；镇静；术后疼痛；阿片依赖、耐受、戒断； 出院]支持更安全的阿片类药物节约方法[手术；镇静；非阿片类药物治疗； 解职]。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，并检查 EPD对应激生物标志物的可能影响，如头发、皮肤、血液中的皮质醇、去甲肾上腺素、 或者尿液。对公众健康的主要好处可能是保护最脆弱的人 防止患者人数对他们未来的健康和福祉造成不必要的损害。

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.