A Novel Deep Learning Model for Pain Intensity Evaluation

Abstract

Pain assessment is a critical component of healthcare, influencing effective pain management, individualized care, identification of underlying issues, and patient satisfaction. However, the subjectivity and limitations of self-reported assessments have led to disparities in pain evaluation, particularly in vulnerable populations such as children, the elderly, individuals with cognitive impairments, and those with mental health conditions. Recent advances in technology and artificial intelligence (AI) have paved the way for innovative solutions in pain intensity evaluation.This paper presents a novel deep learning model to automatically classify pain intensity levels and compares them with six state-of-the-art deep learning classification models - ResNet-50, VGG-19, EfficientNet, DenseNets, Inception, and Xception- using the UNBC-McMaster Shoulder Pain Expression Archive Database for training. Transfer learning is employed to optimize model efficiency and minimize the need for extensive labeled data. Model evaluations are conducted based on accuracy, precision, recall, and F1 score. The proposed model, ZNet, showed superior performance with accuracy of 95.4%, precision and recall of 64.4% and 63.4%, respectively, and F1-score of 63.7%. Furthermore, this study addresses the challenge of accurately evaluating pain intensity in patients who cannot communicate verbally or face language barriers. By harnessing AI technology and facial expression analysis methods, we aim to provide an objective, reliable, and precise pain assessment methodology. Automated artificial based solutions enhance the reliability of pain evaluations, and holds promise for improving decision-making in pain management and treatment processes, ultimately enhancing patients' quality of life.

Keywords

• pain intensity
• deep learning
• facial expression
• classification
• automated recognition

Kaynakça

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