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Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...

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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study.

Insun Park1, Jae Hyon Park2, Jongjin Yoon3

  • 1Department of Anaesthesiology and Pain Medicine, Seoul National University Bundang Hospital, 82, Gumi 173, Bundang, Seongnam, 13620, Gyeonggi, Republic of Korea.

Journal of Clinical Monitoring and Computing
|December 27, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence models analyzing facial expressions can accurately predict significant postoperative pain. This technology shows potential for screening patients requiring immediate pain relief.

Keywords:
Artificial intelligenceFacial recognitionMachine learningNumerical rating scalePostoperative pain

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Area of Science:

  • Medical technology
  • Artificial intelligence
  • Pain management

Background:

  • Assessing postoperative pain is crucial for patient recovery.
  • Objective pain measurement in postoperative patients remains challenging.
  • Facial expressions offer a potential non-invasive indicator of pain.

Purpose of the Study:

  • To evaluate the accuracy of artificial intelligence (AI) models in predicting significant postoperative pain.
  • To determine if facial expressions can serve as reliable indicators of pain intensity after surgery.

Main Methods:

  • 155 facial expressions from gastric cancer surgery patients were analyzed.
  • Machine learning models were developed using facial action units (AUs), gaze, and landmarks.
  • Models predicted significant pain (NRS ≥ 7) versus less significant pain (NRS < 7).

Main Results:

  • Specific AUs (AU17, AU20) showed some association with pain, but less effectively than general facial features.
  • Models using head position and facial landmarks achieved higher prediction accuracy (AUROC 0.85-0.96).
  • A merged AI model incorporating gaze, eye, head, and facial landmarks demonstrated the best performance (AUROC 0.90).

Conclusions:

  • AI models analyzing facial expressions can accurately predict significant postoperative pain.
  • These models hold potential for screening patients needing urgent analgesia.
  • Facial landmarks and head position are more predictive of postoperative pain than specific AUs.