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. 2022 Jul 29;12(8):1831.
doi: 10.3390/diagnostics12081831.

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

Xiaoying Cheng  1 , Huaiyu Zhu  2 , Linli Mei  3 , Feixiang Luo  4 , Xiaofei Chen  5 , Yisheng Zhao  2 , Shuohui Chen  3 , Yun Pan  2
Affiliations

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

Xiaoying Cheng et al. Diagnostics (Basel). .

Abstract

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.

Keywords: artificial intelligence; blood sampling; neonatal pain; on-site assessment; real-world data.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The study design of the clinical practicability of automated NPA systems.
Figure 2
Figure 2
Samples of image sequence in a neonatal pain video.
Figure 3
Figure 3
The framework of the unsupervised-feature-modification part in our AI-NPA model.
Figure 4
Figure 4
Samples of neonatal pain data with different blood-sampling operations and pain scores.
Figure 5
Figure 5
The framework of the automated NPA system and its application.
See this image and copyright information in PMC

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