First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain
It addresses neonatal postoperative pain assessment, which is a critical issue in healthcare, but is incremental as it applies existing deep learning methods to a new medical domain.
This paper tackled the problem of continuously assessing neonatal postoperative pain by developing a fully automated deep learning framework, using a Bilinear CNN with RNN to model temporal patterns, and found a clear difference between acute and postoperative pain patterns.
This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent Neural Network (RNN). Although acute and postoperative pain have some common characteristics (e.g., visual action units), postoperative pain has a different dynamic, and it evolves in a unique pattern over time. Our experimental results indicate a clear difference between the pattern of acute and postoperative pain. They also suggest the efficiency of using a combination of bilinear CNN with RNN model for the continuous assessment of postoperative pain intensity.