Multimodal Assessment of Neonatal Pain Using Computer Vision


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Abstract

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 longterm outcomes. The main aim of this project is to develop a robust and comprehensive automatic system that generates a standardized pain assessment comparable to those obtained by conventional nurse-derived pain scores. The continuous monitoring of pain, using affordable, non-invasive, and easily integrable devices, provides immediate pain detection and intervention, and therefore, contribute to improved long term outcomes; i.e., reduce the outcomes of under- and over-treatment. It can also decrease caregivers’ bias and assessment burden.
While further research is needed, the preliminary results of our research showed that the automatic assessment of neonatal pain is a viable and more efficient alternative to the manual assessment.


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Research Team

Dr. Yu Sun, Associate Professor, CSE, USF
Dr. Dmitry Goldgof, Professor, CSE, USF
Dr. Rangachar Kasturi, Professor, CSE, USF
Dr. Terri Ashmeade, Professor, College of Medicine Pediatrics, USF Health, USF
Dr. Thao Ho, Assistant Professor, College of Medicine Pediatrics, USF Health, USF
Dr. Ghada Zamzmi, Research Fellow, NLM, NIH
Chih-Yun Pai, PhD Student, CSE, USF
Rahul Paul, PhD Student, CSE, USF
Md Sirajus Salekin, PhD Student, CSE, USF
Jacqueline Hausmann, Masters Student, CSE, USF


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