A Comprehensive Investigate of Deep Learning in Facial Pain Prediction

Abstract

Traditional approaches to pain assessment, such as patient self-reporting and clinician observation, have inherent limitations, particularly for non-verbal and vulnerable populations, due to their subjective nature and lack of objectivity. This study delves into the potential of deep learning for facial pain prediction. Five primary model categories are examined: Convolutional Neural Networks (CNNs) like the improved EfficientNet B4S demonstrate 99.7% accuracy in detecting high-intensity pain, while ResNet101 combined with LSTM achieves 86.13% accuracy in binary classification. Spatio-temporal models, exemplified by AHDI, surpass current state-of-the-art methods. Additionally, Transformer-based architectures, point cloud/Graph Neural Networks (GNNs), and multimodal fusion models exhibit promising results. However, challenges persist, including model interpretability issues, limited clinical generalizability, and data annotation bottlenecks. Future research will emphasize explainable AI (XAI), domain adaptation, and lightweight, privacy-preserving model deployment to facilitate the transition from laboratory settings to clinical practice, ultimately benefiting non-verbal patients, and achieving for more equitable, reliable, and ethically responsible pain assessment solutions.