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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.
Insights
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.
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.
Abstract:
It is well-known that severe pain and powerful pain medications cause short- and long-term damage to the developing nervous system of newborns. Caregivers routinely use physiological vital signs [Heart Rate (HR), Respiration Rate (RR), Oxygen Saturation (SR)] to monitor post-surgical pain in the Neonatal Intensive Care Unit (NICU). Here we present a novel approach that combines continuous, non-invasive monitoring of these vital signs and Computer Vision/Deep Learning to make automatic neonate pain detection with an accuracy of 74% AUC, 67.59% mAP. Further, we report for the first time our Early Pain Detection (EPD) approach that explores prediction of the time to onset of post-surgical pain in neonates. Our EPD can alert NICU workers to postoperative neonatal pain about 5 to 10 minutes prior to pain onset. In addition to alleviating the need for intermittent pain assessments by busy NICU nurses via long-term observation, our EPD approach creates a time window prior to pain onset for the use of less harmful pain mitigation strategies. Through effective pain mitigation prior to spinal sensitization, EPD could minimize or eliminate severe post-surgical pain and the consequential need for powerful analgesics in post-surgical neonates.
