A Comprehensive and Context-Sensitive Neonatal Pain Assessment Using Computer Vision

Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several painful procedures during their hospitalization. Assessing neonatal pain is difficult because the current standard for assessment is subjective, inconsistent, and discontinuous. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term outcomes. In this paper, we present a comprehensive pain assessment system that utilizes facial expressions along with crying sounds, body movement, and vital sign changes. The proposed automatic system generates a standardized pain assessment comparable to those obtained by conventional nurse-derived pain scores. The system achieved 95.56% accuracy using decision fusion of different pain responses that were recorded in a challenging clinical environment. In addition to the decision fusion, we present the performance of multimodal assessment using other fusion schemes as well as a unimodal assessment approach. We also discuss the impact of different factors (e.g., gestational age) on pain, propose several group-specific models for pain assessment (e.g., pre-term and full-term models), and compare the performance of these models with the performance of general models. While further research is needed, our results show that the automatic assessment of neonatal pain is a viable and more efficient alternative to the manual assessment.