人工智能在新生儿早期疼痛管理中的未来作用。

Paediatric & Neonatal Pain Pub Date : 2021-08-05 eCollection Date: 2021-09-01 DOI:10.1002/pne2.12060
Md Sirajus Salekin, Peter R Mouton, Ghada Zamzmi, Raj Patel, Dmitry Goldgof, Marcia Kneusel, Sammie L Elkins, Eileen Murray, Mary E Coughlin, Denise Maguire, Thao Ho, Yu Sun
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引用次数: 0

摘要

越来越先进的医疗技术、外科手术和支持性保健措施的出现,提高了早产儿和/或有危及生命的健康状况的婴儿的存活率。在美国,这一趋势与更多的新生儿手术和更高的新生儿重症监护病房(NICU)的入院率有关。手术后,目前NICU的疼痛管理主要依赖麻醉剂(阿片类药物),如吗啡和芬太尼(药效约为吗啡的100倍),这导致许多并发症,包括因阿片类药物戒断而延长在NICU的住院时间。在本文中,我们回顾了目前NICU疼痛评估和治疗的实践和挑战,并概述了未来使用人工智能(AI)支持新生儿疼痛和阿片类药物节约方法的持续努力。新一代nicu疼痛管理方法的主要焦点是主动缓解(避免)疼痛,旨在防止术后疼痛和阿片类药物戒断对新生儿的伤害。基于人工智能的框架可以使用连续客观变量的单一或多个组合,即面部和身体运动、哭泣频率和生理数据(生命体征),对术后镇静后疼痛发作时间做出高置信度的预测。这样的预测将在疼痛发作之前创造一个治疗窗口,以便通过非麻醉性药物和非药物干预措施减轻疼痛。这些新兴的基于人工智能的策略有可能最大限度地减少或避免术后疼痛和阿片类药物戒断对新生儿身体和精神的损害。
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Future roles of artificial intelligence in early pain management of newborns.

The advent of increasingly sophisticated medical technology, surgical interventions, and supportive healthcare measures is raising survival probabilities for babies born premature and/or with life-threatening health conditions. In the United States, this trend is associated with greater numbers of neonatal surgeries and higher admission rates into neonatal intensive care units (NICU) for newborns at all birth weights. Following surgery, current pain management in NICU relies primarily on narcotics (opioids) such as morphine and fentanyl (about 100 times more potent than morphine) that lead to a number of complications, including prolonged stays in NICU for opioid withdrawal. In this paper, we review current practices and challenges for pain assessment and treatment in NICU and outline ongoing efforts using Artificial Intelligence (AI) to support pain- and opioid-sparing approaches for newborns in the future. A major focus for these next-generation approaches to NICU-based pain management is proactive pain mitigation (avoidance) aimed at preventing harm to neonates from both postsurgical pain and opioid withdrawal. AI-based frameworks can use single or multiple combinations of continuous objective variables, that is, facial and body movements, crying frequencies, and physiological data (vital signs), to make high-confidence predictions about time-to-pain onset following postsurgical sedation. Such predictions would create a therapeutic window prior to pain onset for mitigation with non-narcotic pharmaceutical and nonpharmaceutical interventions. These emerging AI-based strategies have the potential to minimize or avoid damage to the neonate's body and psyche from postsurgical pain and opioid withdrawal.

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