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Electrophysiological Measurements and Analysis of Nociception in Human Infants
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Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological

Jacqueline Hausmann1, Jiayi Wang1, Marcia Kneusel1

  • 1University of South Flordia.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system for early detection of infant pain using vital signs, predicting pain onset 5-10 minutes in advance. This allows for timely interventions, potentially reducing the need for strong pain medications in newborns.

Keywords:
deep learningneonatal painneural networkspain predictionvital signs

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Area of Science:

  • Neonatal care
  • Artificial Intelligence in Medicine
  • Pain Management

Background:

  • Neonatal pain and analgesics can harm developing nervous systems.
  • Current pain monitoring relies on vital signs (HR, RR, SR) and intermittent assessments.

Purpose of the Study:

  • To develop an automated system for neonate pain detection using vital signs and deep learning.
  • To introduce an Early Pain Detection (EPD) approach for predicting pain onset in neonates.

Main Methods:

  • Continuous, non-invasive monitoring of vital signs (HR, RR, SR).
  • Integration with Computer Vision and Deep Learning algorithms for pain detection.
  • Development of the Early Pain Detection (EPD) predictive model.

Main Results:

  • Achieved 74% AUC and 67.59% mAP for automatic neonate pain detection.
  • The EPD approach predicts pain onset 5-10 minutes in advance.
  • Demonstrated potential to reduce reliance on subjective pain assessments and strong analgesics.

Conclusions:

  • AI-powered vital sign monitoring offers accurate and early detection of neonatal pain.
  • EPD provides a crucial time window for proactive, less harmful pain management strategies.
  • This technology can significantly improve outcomes for post-surgical neonates by minimizing pain and analgesic exposure.