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Pain

Pain

Pain serves as a critical warning signal that alerts the body to potential or actual harm. When mechanical pressure on the skin is intense, such as from a sharp pinch, the sensation transitions from touch to pain. Similarly, extreme temperatures, like a hot pot handle, convert the sensation of heat into pain. Pain can also result from overstimulation of other senses, such as blinding light, loud noise, or the intense heat from habañero peppers. This ability to sense pain is essential for...

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Electrophysiological Measurements and Analysis of Nociception in Human Infants

Electrophysiological Measurements and Analysis of Nociception in Human Infants

Published on: December 20, 2011

Intelligent system for infants' pain detection: pain intensity estimation using deep learning approach.

Mashhour Amer¹, Manal Kassab², Waed Alshurman³

¹Department of Biomedical Engineering, Jordan University for Science and Technology, AR-Ramtha, Irbid, 3030, Jordan.

Physical and Engineering Sciences in Medicine

|March 17, 2026

Summary

This study introduces an Artificial Intelligence (AI) model to detect infant pain using facial expressions. The AI system accurately estimates pain levels, improving pain management for non-verbal infants in the NICU.

Keywords:

Deep learning Pain detection Pain intensity Regression Transfer learning

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Area of Science:

Medical technology
Artificial Intelligence
Neonatal care

Background:

Traditional pain assessment tools for infants have limitations.
Accurate pain detection is crucial for effective pain management in neonates.
Artificial Intelligence offers novel approaches for objective pain assessment.

Purpose of the Study:

To develop a deep learning model for estimating infant pain intensity (0-9) from facial expressions.
To address limitations of traditional pain assessment methods in the NICU.
To improve pain management for non-verbal infants.

Main Methods:

A regression Convolutional Neural Network (CNN) model was developed.
Transfer learning with a pre-trained VGG16 model was employed for fine-tuning.
Facial expressions of full-term infants during NICU procedures were analyzed.

Main Results:

The CNN model achieved a Mean Absolute Error (MAE) of 0.494 and Mean Squared Error (MSE) of 0.435.
The model demonstrated good performance in estimating pain intensity.
Transfer learning and fine-tuning helped improve model performance and avoid overfitting.

Conclusions:

The proposed AI model accurately detects infant pain based on facial expressions.
This technology can significantly aid healthcare professionals in providing effective pain management.
Future work includes developing a portable, user-friendly web application for the system.