Pain monitoring
Project data
Department in charge
Some premature infants spend weeks in incubators. During this time, their nervous system continues to develop. It is medically proven that prolonged periods of pain during this phase impact negatively the development of their white matter. However, detecting and monitoring these painful periods would require constant attention from caregivers, which is practically unfeasible.
In response, our project aims to develop an automatic, non-contact pain monitor. This device will use a camera to observe the infant in the incubator and assess their pain level using a standardized clinical pain scale, notifying caregivers as necessary. This innovation will help reduce the duration of painful periods, enhancing patient satisfaction, parental confidence, and service provider outcomes.
As part of the project, we are creating new AI-based algorithms and end-user hardware devices to enable data collection in hospital settings and ensure continuous, objective evaluation of the infant's condition according to a clinical pain scale.
Project goals
Reducing pain in infants helps minimize brain damage and trauma in premature babies. This, in turn, leads to fewer anxious or traumatized children, as research has shown a connection between the development of ADHD, special educational needs (SEN), and early caregiving-related trauma.
As part of the project, we are developing new AI-based algorithms as well as end-user hardware devices. These tools will facilitate data collection in hospital settings and enable continuous, objective evaluation of the infant's condition based on a standardized pain scale.
Project results
Pain scale measurements are widely discussed in the literature. While their measurability has been proven, currently no practical implementation exists. Our innovation focuses on developing an algorithm package capable of quantifying the pain of the observed infants. The project aims to create clinically validated and CE-certified devices, ready for deployment.
Previously, we have published development results that enabled us to automatically determine vital parameters and caregiving conditions of infants under continuous camera observation using artificial intelligence and computer vision algorithms. Building on these technologies, we are integrating proven AI and computer vision methods to develop a clinically tested solution. This solution will automatically classify continuously monitored infants into the most commonly used pain scale, N-PASS.
The planned clinical testing sites are the Neonatal Intensive Care Unit of Semmelweis University in Hungary and the Division of Neonatology at Turku University Hospital in Turku, Finland.