# First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

arXiv: https://arxiv.org/abs/2003.10601 · HTML: https://mlsanity.com/paper/2003.10601/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Robust Neonatal Face Detection in Real-world Clinical Settings](https://mlsanity.com/paper/2204.00655/) (2022)
- [Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos](https://mlsanity.com/paper/2604.25680/) (2026)

## Abstract (arXiv)

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.