Multi-Channel Neural Network for Assessing Neonatal Pain from Videos
This work addresses neonatal pain assessment, a critical issue in healthcare, by providing an automated and objective method, though it appears incremental as it builds on existing deep learning techniques.
The paper tackled the problem of subjective and inconsistent neonatal pain assessment by proposing a multi-channel deep learning framework that integrates facial expression and body movement from videos, achieving superior results compared to existing methods.
Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.