Systemic opioid regimens for postoperative pain in neonates

Kinoshita, Mari; Nascimento, Israel Júnior Borges do; Bruschettini, Matteo; Styrmisdóttir, Lea

doi:10.1002/14651858.cd015016

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…While acute pain management can be addressed using various medication options ranging from over-the-counter drugs to opioids, chronic pain management primarily relies on psychiatric interventions to control the underlying cause, which may sometimes even require surgical interventions. The authors of [ 54 , 55 , 56 ] conducted a study on chronic pain detection and followed patients with spinal/brain injuries for six months. In contrast, the authors of [ 50 ] focused on acute MI pain and achieved the highest overall accuracy using their CNN model.…”

Section: Review Of the Studiesmentioning

confidence: 99%

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

et al. 2023

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Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

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