Automated Pain Assessment in Neonates

Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling

Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool for real-world blood sampling. Method: We performed a prospective study to analyze the consistency of the NPA results given by a self-developed automated NPA system and nurses’ on-site NPAs (OS-NPAs) for 232 newborns during blood sampling in neonatal wards, where the neonatal infant pain scale (NIPS) was used for evaluation. Spearman correlation analysis and the degree of agreement of the pain score and pain grade derived by the NIPS were applied for statistical analysis. Results: Taking the OS-NPA results as the gold standard, the accuracies of the NIPS pain score and pain grade given by the automated NPA system were 88.79% and 95.25%, with kappa values of 0.92 and 0.90 (p < 0.001), respectively. Conclusion: The results of the automated NPA system for real-world neonatal blood sampling are highly consistent with the results of the OS-NPA. Considering the great advantages of automated NPA systems in repeatability, efficiency, and cost, it is worth popularizing the AI technique in NPA for precise and efficient neonatal pain management.

Automatic and Continuous Discomfort Detection for Premature Infants in a NICU Using Video-Based Motion Analysis

Frequent pain and discomfort in premature infants can lead to long-term adverse neurodevelopmental outcomes. Video-based monitoring is considered to be a promising contactless method for identification of discomfort moments. In this study, we propose a video-based method for automated detection of infant discomfort. The method is based on analyzing facial and body motion. Therefore, motion trajectories are estimated from frame to frame using optical flow. For each video segment, we further calculate the motion acceleration rate and extract 18 time- and frequency-domain features characterizing motion patterns. A support vector machine (SVM) classifier is then applied to video sequences to recognize infant status of comfort or discomfort. The method is evaluated using 183 video segments for 11 infants from 17 heel prick events. Experimental results show an AUC of 0.94 for discomfort detection and the average accuracy of 0.86 when combining all proposed features, which is promising for clinical use.

Multimodal pain assessment improves discrimination between noxious and non‐noxious stimuli in infants

Infants in neonatal intensive care units frequently experience clinically necessary painful procedures, which elicit a range of behavioral, physiological, and neurophysiological responses. However, the measurement of pain in this population is a challenge and no gold standard exists. The aim of this study was to investigate how noxious‐evoked changes in facial expression, reflex withdrawal, brain activity, heart rate, and oxygen saturation are related and to examine their accuracy in discriminating between noxious and non‐noxious stimuli. In 109 infants who received a clinically required heel lance and a control non‐noxious stimulus, we investigated whether combining responses across each modality, or including multiple measures from within each modality improves our ability to discriminate the noxious and non‐noxious stimuli. A random forest algorithm was used to build data‐driven models to discriminate between the noxious and non‐noxious stimuli in a training set which were then validated in a test set of independent infants. Measures within each modality were highly correlated, while different modalities showed less association. The model combining information across all modalities had good discriminative ability (accuracy of 0.81 in identifying noxious and non‐noxious stimuli), which was higher than the discriminative power of the models built from individual modalities. This demonstrates the importance of including multiple modalities in the assessment of infant pain.

Postoperative Pain Management in Pediatric Patients Undergoing Cardiac Surgery: Where Are We Heading?

OBJECTIVES

Adequate postoperative pain management is crucial in pediatric patients undergoing cardiac surgery because pain can lead to devastating short- and long-term consequences. This review discusses the limitations of current postoperative pain assessment and management in children after cardiac surgery, the obstacles to providing optimal treatment, and concepts to consider that may overcome these barriers.

DATA SOURCE

MEDLINE and PubMed.

CONCLUSIONS

Effective pain management in infants and young children undergoing cardiac surgery continues to evolve with innovative methods of both assessment and therapy using newer drugs or novel routes of administration. Artificial intelligence- and machine learning-based pain assessment and patient-tailored management in both pain measurement and prevention are already being integrated into the routine of current practice.