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Neonates are not able to verbally communicate pain, hindering the correct identification of this phenomenon. Several clinical scales have been proposed to assess pain, mainly using the facial features of the neonate, but a better comprehension of these features is yet required, since several related works have shown the subjectivity of these scales. Meanwhile, computational methods have been implemented to automate neonatal pain assessment and, although performing accurately, these methods still lack the interpretability of the corresponding decision-making processes. To address this issue, we propose in this work a facial feature extraction framework to gather information and investigate the human and machine neonatal pain assessments, comparing the visual attention of the facial features perceived by health-professionals and parents of neonates with the most relevant ones extracted by eXplainable Artificial Intelligence (XAI) methods, considering the VGG-Face and N-CNN deep learning architectures. Our experimental results show that the information extracted by the computational methods are clinically relevant to neonatal pain assessment, but yet do not agree with the facial visual attention of health-professionals and parents, suggesting that humans and machines can learn from each other to improve their decision-making processes. We believe that these findings might advance our understanding of how humans and machines code and decode neonatal facial responses to pain, enabling further improvements in clinical scales widely used in practical situations and in face-based automatic pain assessment tools as well.

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Neonatal Facial Pain Assessment (NFPA) is essential to improve neonatal pain management. Pose variation and occlusion, which can significantly alter the facial appearance, are two major and still unstudied barriers to NFPA. We bridge this gap in terms of method and dataset. Techniques to tackle both challenges in other tasks either expect pose/occlusion-invariant deep learning methods or first generate a normal version of the input image before feature extraction, combining these we argue that it is more effective to jointly perform adversarial learning and end-to-end classification for their mutual benefit. To this end, we propose a Pose-invariant Occlusion-robust Pain Assessment (POPA) framework, with two novelties. We incorporate adversarial learning-based disturbance mitigation for end-to-end pain-level classification and propose a novel composite loss function for facial representation learning; compared to the vanilla discriminator that implicitly determines occlusion and pose conditions, we propose a multi-scale discriminator that determines explicitly, while incorporating local discriminators to enhance the discrimination of key regions. For a comprehensive evaluation, we built the first neonatal pain dataset with disturbance annotation involving 1091 neonates and also applied the proposed POPA to the facial expression recognition task. Extensive qualitative and quantitative experiments prove the superiority of the POPA.

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BACKGROUND: Neonatal pain assessment (NPA) represents a huge global problem of essential importance, as a timely and accurate assessment of neonatal pain is indispensable for implementing pain management. PURPOSE: To investigate the consistency of pain scores derived through video-based NPA (VB-NPA) and on-site NPA (OS-NPA), providing the scientific foundation and feasibility of adopting VB-NPA results in a real-world scenario as the gold standard for neonatal pain in clinical studies and labels for artificial intelligence (AI)-based NPA (AI-NPA) applications. SETTING: A total of 598 neonates were recruited from a pediatric hospital in China. METHODS: This observational study recorded 598 neonates who underwent one of 10 painful procedures, including arterial blood sampling, heel blood sampling, fingertip blood sampling, intravenous injection, subcutaneous injection, peripheral intravenous cannulation, nasopharyngeal suctioning, retention enema, adhesive removal, and wound dressing. Two experienced nurses performed OS-NPA and VB-NPA at a 10-day interval through double-blind scoring using the Neonatal Infant Pain Scale to evaluate the pain level of the neonates. Intra-rater and inter-rater reliability were calculated and analyzed, and a paired samples t-test was used to explore the bias and consistency of the assessors' pain scores derived through OS-NPA and VB-NPA. The impact of different label sources was evaluated using three state-of-the-art AI methods trained with labels given by OS-NPA and VB-NPA, respectively. RESULTS: The intra-rater reliability of the same assessor was 0.976-0.983 across different times, as measured by the intraclass correlation coefficient. The inter-rater reliability was 0.983 for single measures and 0.992 for average measures. No significant differences were observed between the OS-NPA scores and the assessment of an independent VB-NPA assessor. The different label sources only caused a limited accuracy loss of 0.022-0.044 for the three AI methods. CONCLUSION: VB-NPA in a real-world scenario is an effective way to assess neonatal pain due to its high intra-rater and inter-rater reliability compared to OS-NPA and could be used for the labeling of large-scale NPA video databases for clinical studies and AI training.

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AIM: The aim of this study was to investigate psychometric properties, reliability and validity, of Astrid Lindgren and Lund Children's Hospitals Pain and Stress Assessment Scale for Preterm and Sick Newborn Infants (ALPS-Neo), as a measure for procedural pain. METHODS: This observational, prospective study with a repeated measures design, explored inter-rater reliability by two raters assessing 21 neonates during non-pain and pain events. Construct validity was explored, that is, ability to discriminate between non-pain and pain, and criterion validity by correlating ALPS-Neo with Premature Infant Pain Profile-Revised (PIPP-R) and Skin Conductance Algesimeter (SCA) in 54 neonates without ventilator support and sedation undergoing routine heel-stick procedure in a tertiary neonatal intensive care unit. RESULTS: Mean gestational and assessment age of 54 infants was 33.8 weeks and 12.7 days respectively. Inter-rater reliability from baseline, skin wiping, heel-stick events for 21 infants demonstrated intraclass correlations with 95% confidence intervals (CI) of 0.49 (-0.27 to 0.79), 0.86 (0.65-0.94) and 0.73 (0.34-0.89) respectively. ALPS-Neo discriminated significantly between baseline, non-pain and heel-stick (mean differences from pain event -2.3 and -1.0 respectively) and correlated during heel-stick with PIPP-R (r = 0.56, 95% CI: 0.34-0.72), not with SCA. CONCLUSION: ALPS-Neo may be used as a measure for procedural pain.

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Background: The assessment and management of neonatal pain is crucial for the development and wellbeing of vulnerable infants. Specifically, neonatal pain is associated with adverse health outcomes but is often under-identified and therefore under-treated. Neonatal stress may be misinterpreted as pain and may therefore be treated inappropriately. The assessment of neonatal pain is complicated by the non-verbal status of patients, age-dependent variation in pain responses, limited education on identifying pain in premature infants, and the clinical utility of existing tools. Objective: We review research surrounding neonatal pain assessment scales currently in use to assess neonatal pain in the neonatal intensive care unit. Methods: We performed a systematic review of original research using PRISMA guidelines for literature published between 2016 and 2021 using the key words "neonatal pain assessment" in the databases Web of Science, PubMed, and CINAHL. Fifteen articles remained after review, duplicate, irrelevant, or low-quality articles were eliminated. Results: We found research evaluating 13 neonatal pain scales. Important measurement categories include behavioral parameters, physiological parameters, continuous pain, acute pain, chronic pain, and the ability to distinguish between pain and stress. Provider education, inter-rater reliability and ease of use are important factors that contribute to an assessment tool's success. Each scale studied had strengths and limitations that aided or hindered its use for measuring neonatal pain in the neonatal intensive care unit, but no scale excelled in all areas identified as important for reliably identifying and measuring pain in this vulnerable population. Conclusion: A more comprehensive neonatal pain assessment tool and more provider education on differences in pain signals in premature neonates may be needed to increase the clinical utility of pain scales that address the different aspects of neonatal pain.

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Preterm and sick newborn infants undergo several painful procedures during their hospital stay, potentially leading to short- and long-term negative consequences. Pain assessment should be performed regularly to provide optimal pain management. Nurses' knowledge of and attitude toward neonatal pain assessment affect how pain is assessed and managed in the clinical situation. The aim of this study was to explore Swedish nurses' perception, knowledge, and use of neonatal pain assessment. This descriptive, cross-sectional questionnaire study was conducted across all Swedish neonatal units (n = 38). Respondents were chosen through convenience sampling by the head nurses at each unit. Ten nurses from each unit were asked to complete the survey, which contained both closed and open questions. A majority of the units (30/38; 79%) participated and 232 surveys were returned, a response rate of 61%. Of the nurses, 91% thought that neonatal pain assessment was important. Many nurses mentioned various difficulties with pain assessment and concerns that the scales used might not assess pain correctly. About half of the nurses considered themselves to have enough knowledge of neonatal pain assessment. Those who reported having enough knowledge of pain assessment viewed the pain scales used at their units more positively. Of the nurses, 74% reported using a pain assessment scale several times per work shift. Pain management guidelines were available according to 75% of nurses, but only 53% reported that the guidelines were followed. Although nurses in general expressed a positive attitude toward pain assessment scales, this was not necessarily evident in their clinical practice. Lack of knowledge, available or accessible guidelines, or concerns regarding the validity of available pain scales seemed to limit their use.

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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.