Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

Area of Science:

• Medical technology
• Pain assessment
• Neonatal care

Background:

• Current neonatal postoperative pain assessment by caregivers is subjective, inconsistent, and slow.
• Existing automated methods are unimodal and primarily focus on acute pain, not prolonged postoperative pain.
• Pain is multimodal, necessitating approaches that integrate various signal types for accurate assessment.

Purpose of the Study:

• To develop and evaluate a novel multimodal spatio-temporal approach for assessing neonatal postoperative pain.
• To compare the performance of the proposed multimodal approach against unimodal methods.
• To determine the impact of integrating temporal information on pain assessment accuracy.

Main Methods:

• Developed a multimodal approach integrating visual and vocal signals for pain assessment.
• Employed spatio-temporal analysis to enhance stability and reduce misclassification errors.
• Conducted experiments on a real-world dataset to validate the approach's effectiveness.

Main Results:

• The multimodal spatio-temporal approach achieved an AUC of 0.87 and 79% accuracy.
• Performance was significantly higher (average 6.67% AUC, 6.33% accuracy) compared to unimodal approaches.
• Integration of temporal information notably improved performance by capturing pain dynamics.

Conclusions:

• The proposed multimodal spatio-temporal approach offers a reliable and automated method for neonatal postoperative pain assessment.
• This technology can serve as a viable alternative to manual assessment, advancing automated pain monitoring in clinical settings.
• The findings support the potential for improved point-of-care testing and home-based pain monitoring solutions.