A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.

Video-Based Neonatal Pain Assessment in Uncontrolled Conditions.

OBJECTIVE: Neonatal pain can have long-term adverse effects on newborns' cognitive and neurological development. Video-based Neonatal Pain Assessment (NPA) method has gained increasing attention due to its performance and practicality. However, existing methods focus on assessment under controlled environments while ignoring real-life disturbances present in uncontrolled conditions. METHODS: We propose a video-based NPA method, which is robust to four real-life disturbances and adaptively highlights keyframes. Our method involves a region-channel-attention module for extracting facial features under the disturbances of facial occlusion and pose variation; a body language analysis module robust to disturbances from body occlusion and movement interference, which utilizes skeleton sequences to represent the neonate's body; and a keyframes-aware convolution to get rid of information located at non-contributing moments. For evaluation, we built an NPA video dataset of 1091 neonates with disturbance annotations. RESULTS: The results show that our method consistently outperforms state-of-the-art methods on the full dataset and nine subsets, where it achieves an accuracy of 91.04% on the full dataset with an accuracy increment of 6.27%. Contributions: We present the problem of video-based NPA under uncontrolled conditions, propose a method robust to four disturbances, and construct a video NPA dataset, thus facilitating the practical applications of NPA.

Pose-invariant and occlusion-robust neonatal facial pain assessment.

Neonatal Facial Pain Assessment (NFPA) is essential to improve neonatal pain management. Pose variation and occlusion, which can significantly alter the facial appearance, are two major and still unstudied barriers to NFPA. We bridge this gap in terms of method and dataset. Techniques to tackle both challenges in other tasks either expect pose/occlusion-invariant deep learning methods or first generate a normal version of the input image before feature extraction, combining these we argue that it is more effective to jointly perform adversarial learning and end-to-end classification for their mutual benefit. To this end, we propose a Pose-invariant Occlusion-robust Pain Assessment (POPA) framework, with two novelties. We incorporate adversarial learning-based disturbance mitigation for end-to-end pain-level classification and propose a novel composite loss function for facial representation learning; compared to the vanilla discriminator that implicitly determines occlusion and pose conditions, we propose a multi-scale discriminator that determines explicitly, while incorporating local discriminators to enhance the discrimination of key regions. For a comprehensive evaluation, we built the first neonatal pain dataset with disturbance annotation involving 1091 neonates and also applied the proposed POPA to the facial expression recognition task. Extensive qualitative and quantitative experiments prove the superiority of the POPA.

Video-Based versus On-Site Neonatal Pain Assessment in Neonatal Intensive Care Units: The Impact of Video-Based Neonatal Pain Assessment in Real-World Scenario on Pain Diagnosis and Its Artificial Intelligence Application.

BACKGROUND: Neonatal pain assessment (NPA) represents a huge global problem of essential importance, as a timely and accurate assessment of neonatal pain is indispensable for implementing pain management. PURPOSE: To investigate the consistency of pain scores derived through video-based NPA (VB-NPA) and on-site NPA (OS-NPA), providing the scientific foundation and feasibility of adopting VB-NPA results in a real-world scenario as the gold standard for neonatal pain in clinical studies and labels for artificial intelligence (AI)-based NPA (AI-NPA) applications. SETTING: A total of 598 neonates were recruited from a pediatric hospital in China. METHODS: This observational study recorded 598 neonates who underwent one of 10 painful procedures, including arterial blood sampling, heel blood sampling, fingertip blood sampling, intravenous injection, subcutaneous injection, peripheral intravenous cannulation, nasopharyngeal suctioning, retention enema, adhesive removal, and wound dressing. Two experienced nurses performed OS-NPA and VB-NPA at a 10-day interval through double-blind scoring using the Neonatal Infant Pain Scale to evaluate the pain level of the neonates. Intra-rater and inter-rater reliability were calculated and analyzed, and a paired samples t-test was used to explore the bias and consistency of the assessors' pain scores derived through OS-NPA and VB-NPA. The impact of different label sources was evaluated using three state-of-the-art AI methods trained with labels given by OS-NPA and VB-NPA, respectively. RESULTS: The intra-rater reliability of the same assessor was 0.976-0.983 across different times, as measured by the intraclass correlation coefficient. The inter-rater reliability was 0.983 for single measures and 0.992 for average measures. No significant differences were observed between the OS-NPA scores and the assessment of an independent VB-NPA assessor. The different label sources only caused a limited accuracy loss of 0.022-0.044 for the three AI methods. CONCLUSION: VB-NPA in a real-world scenario is an effective way to assess neonatal pain due to its high intra-rater and inter-rater reliability compared to OS-NPA and could be used for the labeling of large-scale NPA video databases for clinical studies and AI training.

Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling.

Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool for real-world blood sampling. Method: We performed a prospective study to analyze the consistency of the NPA results given by a self-developed automated NPA system and nurses' on-site NPAs (OS-NPAs) for 232 newborns during blood sampling in neonatal wards, where the neonatal infant pain scale (NIPS) was used for evaluation. Spearman correlation analysis and the degree of agreement of the pain score and pain grade derived by the NIPS were applied for statistical analysis. Results: Taking the OS-NPA results as the gold standard, the accuracies of the NIPS pain score and pain grade given by the automated NPA system were 88.79% and 95.25%, with kappa values of 0.92 and 0.90 (p < 0.001), respectively. Conclusion: The results of the automated NPA system for real-world neonatal blood sampling are highly consistent with the results of the OS-NPA. Considering the great advantages of automated NPA systems in repeatability, efficiency, and cost, it is worth popularizing the AI technique in NPA for precise and efficient neonatal pain management.