Pain Detection in Masked Faces during Procedural Sedation

TL;DR

This study develops a deep learning model to detect pain in masked faces during medical procedures, achieving high accuracy and demonstrating the potential of computer vision for pain monitoring in occluded facial scenarios.

Contribution

It introduces a novel dataset of masked faces during procedures and trains a deep learning model that outperforms baselines for pain detection in occluded faces.

Findings

Model achieved AP of 0.72 and AUC of 0.82.

Deep learning effectively detects pain in masked faces.

Cross-dataset testing reveals differences in pain expressions.

Abstract

Pain monitoring is essential to the quality of care for patients undergoing a medical procedure with sedation. An automated mechanism for detecting pain could improve sedation dose titration. Previous studies on facial pain detection have shown the viability of computer vision methods in detecting pain in unoccluded faces. However, the faces of patients undergoing procedures are often partially occluded by medical devices and face masks. A previous preliminary study on pain detection on artificially occluded faces has shown a feasible approach to detect pain from a narrow band around the eyes. This study has collected video data from masked faces of 14 patients undergoing procedures in an interventional radiology department and has trained a deep learning model using this dataset. The model was able to detect expressions of pain accurately and, after causal temporal smoothing, achieved an average precision (AP) of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.82. These results outperform baseline models and show viability of computer vision approaches for pain detection of masked faces during procedural sedation. Cross-dataset performance is also examined when a model is trained on a publicly available dataset and tested on the sedation videos. The ways in which pain expressions differ in the two datasets are qualitatively examined.