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.

Engineering Team:

Dr. Yu Sun, Professor, Computer Science and Engineering, USF
Dr. Dmitry Goldgof, Professor, Computer Science and Engineering, USF
Jacqueline Hausmann, PhD Student, Computer Science and Engineering, USF
Md Imran Hossain, PhD Student, Computer Science and Engineering, USF
Jiayi Wang, PhD Student, Computer Science and Engineering, USF


Medical Team:

Dr. Thao Ho, Assistant Professor, College of Medicine Pediatrics, USF Health, USF
Dr. Stephanie Prescott, Assistant Professor, College of Nursing, USF Health, USF
Dr. Denise Maguire, Associate Professor, College of Nursing, USF Health, USF
Dr. Yangxin Huang, Professor, College of Public Health, USF Health, USF
Marcia Kneusel, RN, Clinical Research Nurse, College of Medicine Pediatrics, USF Health, USF


Collaborators:

Dr. Md Sirajus Salekin, Applied Scientist, Amazon
Dr. Ghada Zamzmi, Research Fellow, NLM, NIH
Dr. Peter Mouton, Director and Chief Scientific Officer, SRC Biosciences
Dr. Mark Last, Professor and Director of Data Science Research Center, Ben-Gurion University of the Negev, Israel
Dr. Kanwaljeet S. Anand, Professor of Pediatrics, Stanford University, CA, United States


Previous Team Members:

Dr. Terri Ashmeade, Professor, College of Medicine Pediatrics, USF Health, USF
Dr. Rangachar Kasturi, Professor, CSE, USF
Chih-Yun Pai, Master's Student, CSE, USF


Patents

  • US-11631280-B2, "System and Method for Multimodal Spatiotemporal Pain Assessment", April 2023.
  • US-20230309915-A1, "System and Method for Attentional Multimodal Pain Estimation", October 2023.
  • US-11202604-B2, "Comprehensive and Context-sensitive Neonatal Pain Assessment System and Methods Using Multiple Modalities", December 2021.
  • US-20210052215-A1, "System and Method for Multimodal Spatiotemporal Pain Assessment", Feburary 2021.
  • US-20210030354-A1, "Neonatal Pain Identification From Neonatal Facial Expressions", Feburary 2021.
  • US-10827973-B1, "Machine-based Infants Pain Assessment Tool", November 2020.
  • US-20190320974-A1, "Comprehensive and Context-sensitive Neonatal Pain Assessment System and Methods Using Multiple Modalities", October 2020.

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