Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks

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.