Summary:
Researchers in the UCLA Semel Institute for Neuroscience and Human Behavior have developed a novel, non-invasive pain measurement tool for neonates.
Background:
Pain management in neonatal care remains a critical unmet need. Approximately 90% of premature infants undergo painful procedures, yet pain is only reported in 45% of cases. Current assessments rely heavily on subjective interpretation of behavioral and physiological indicators such as facial expressions, crying, and heart rate fluctuations. This subjective approach often leads to inconsistent pain management in nonverbal patients. Inadequate pain management in neonates has significant consequences including altered neural development, increased stress responses, and long-term cognitive and behavioral impairments. Studies show that unmanaged pain can disrupt the neurodevelopmental pathways and increase complications such as intraventricular hemorrhage and poor weight gain. An objective, reliable pain assessment tool is urgently needed to improve neonatal care outcomes and promote healthier development.
Innovation:
UCLA researchers in the Semel Institute for Neuroscience and Human Behavior have developed a non-invasive, algorithm-based approach to objectively detect infant pain. The algorithm leverages acoustic signal processing and machine learning to analyze crying patterns and predict the probability of pain. This algorithm was trained on a comprehensive database of infant cries, including those recorded during routine vaccinations and other acutely painful stimuli. The algorithm can distinguish pain-related cries from non-painful distress signals with a 90% accuracy. This algorithm can be seamlessly integrated into current clinical settings, commercially available baby monitors, or smartphone apps to improve neonatal care outcomes, reduce stress, enhance neural development, and support overall healthy growth. By providing an objective, real-time assessment tool, this innovation has the potential to standardize neonatal pain evaluation, ensure timely and appropriate pain management strategies.
Potential Applications:
• Neonatal care
• AI infant Monitoring
• Smart baby monitors
• NICU device & patient monitoring
• Smart-phone app for generalized pain monitoring
• Assessment of pain in non-verbal patients with neurological disorders, developmental disabilities, or post-surgical conditions
• Wearable devices
Advantages:
• Objective pain detection
• Non-invasive and real-time analysis
• Improved pain management in neonatal care
• Standardization across clinical settings
• High accuracy of distinguishing pain
• Decreased chance of developmental consequences due to pain
• Potential for remote and parental use
State of Development:
This project is associated with the previous IP disclosure: 2016-171 TRANSLATING INFANT VOCALIZATIONS which was filed in 09/13/2015.
Reference:
UCLA Case No. 2018-327
Relevant Papers:
1. Parga, J.J., Lewin, S., Lewis, J. et al. Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?. Pediatr Res 87, 576–580 (2020). https://doi.org/10.1038/s41390-019-0592-4
2. Bruschettini M, Olsson E, Persad E, Garratt A, Soll R. Clinical rating scales for assessing pain in newborn infants. Cochrane Database Syst Rev. 2022 Mar 18;2022(3):MR000064. doi: 10.1002/14651858.MR000064. PMCID: PMC8996463.
Lead Inventor:
Ariana Anderson, UCLA Assistant Professor at the Semel Institute for Neuroscience and Human Behavior