Future roles of artificial intelligence in early pain management of newborns. Issue 3 (5th August 2021)

• Record Type: Journal Article
• Authors: Salekin, Md Sirajus; Mouton, Peter R.; Zamzmi, Ghada; Patel, Raj; Goldgof, Dmitry; Kneusel, Marcia; Elkins, Sammie L.; Murray, Eileen; Coughlin, Mary E.; Maguire, Denise; Ho, Thao; Sun, Yu
• Other Names: Andersen Randi Dovland guestEditor.
• Is Part Of: Paediatric & neonatal pain. Volume 3:Issue 3(2021)
• Page Start: 134
• Page End: 145
• Publication Date: 2021-08-05
• Subjects: neonatal intensive care unit -- neonatal pain assessment -- neonatal pain prediction -- newborn pain management -- opioid‐based pain management; Pain in children -- Periodicals; Pain in infants -- Periodicals; 616.047208
• DOI: 10.1002/pne2.12060
• Languages: English
• ISSNs: 2637-3807

Abstract

Abstract: 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.