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Machine learning methods on BioVid heat pain database for pain intensity estimation.

Melpo Pittara1, Andreas Anastasiou2,3, Konstantinos Andreou2,3

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Summary
This summary is machine-generated.

This study developed objective pain assessment using machine learning on facial expressions. Convolutional Neural Networks (CNNs) showed the highest accuracy in detecting pain intensity, offering potential for non-communicative patients.

Keywords:
Deep learningImage processingMachine learningPain assessmentTransformers

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Pain Management Research

Background:

  • Traditional pain scales (NRS, VAS, VRS) are subjective and unreliable for certain patient groups.
  • Objective pain assessment is crucial for accurate treatment and improved patient quality of life.
  • Machine learning offers a promising avenue for developing objective pain measurement tools.

Purpose of the Study:

  • To introduce and evaluate an objective pain measurement model using machine learning techniques.
  • To compare the performance of Convolutional Neural Networks (CNNs), VGG16, Convolutional Vision Transformer (CvT), and MobileViT in classifying pain intensity from facial expressions.
  • To assess the potential of automated pain assessment for clinical applications, especially for non-communicative patients.

Main Methods:

  • Utilized facial images from the BioVid Heat Pain Database, capturing peak pain and no-pain moments.
  • Trained and evaluated four machine learning models: CNN, VGG16, CvT, and MobileViT.
  • Compared model performance based on accuracy in classifying pain intensity.

Main Results:

  • The CNN model achieved the highest average accuracy (0.71) in classifying pain intensity.
  • The CvT model demonstrated strong performance with an average accuracy of 0.69.
  • VGG16 (0.56) and MobileViT50 (0.60) showed significantly lower accuracy, indicating challenges in classifying pain from facial images.

Conclusions:

  • Machine learning models, particularly CNNs, show significant potential for objective and consistent pain assessment.
  • Automated facial expression analysis can aid in pain evaluation for non-verbal or cognitively impaired patients.
  • Future research should focus on multimodal data integration to enhance pain detection system robustness.