Deep Learning-Based Instance Appraisable Model (EDi Pain) for Pain Estimation via Facial Videos: A Retrospective Analysis and a Prospective Emergency Department Study.

Pain assessment is a critical aspect of medical care, yet automated systems for clinical pain estimation remain rare. Tools such as the visual analog scale (VAS) are commonly used in emergency departments (EDs) but rely on subjective self-reporting, with pain intensity often fluctuating during triage. An effective automated system should utilize objective labels from healthcare professionals and identify key frames from video sequences for accurate inference. In this study, short video clips were treated as instance segments for the model, with ground truth (physician-rated VAS) provided at the video level. To address the weak label problem, we proposed flexible multiple instance learning approaches. Using a specialized loss function and sampling strategy, our instance-appraisable model, EDi Pain, was trained to estimate pain intensity while evaluating the significance of each instance segment. During inference, the VAS pain score for the entire video is derived from instance-level predictions. In retrospective analysis using the public UNBC-McMaster dataset, the EDi Pain model demonstrated competitive performance relative to prior studies, achieving strong performance in video-level pain intensity estimation, with a mean absolute error (MAE) of 1.85 and a Pearson correlation coefficient (PCC) of 0.63. Additionally, our model was validated on a prospectively collected dataset of 931 patients from National Taiwan University Hospital, yielding an MAE of 1.48 and a PCC of 0.22. In summary, we developed and validated a novel deep learning-based, instance-appraisable model for pain intensity estimation using facial videos. The EDi Pain model shows promise for real-time application in clinical settings, offering a more objective and dynamic approach to pain assessment.