Related Experiment Video
Updated: Sep 14, 2026
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
Published on: May 5, 2011
Automatic quantification of thermographic images of complex regional pain syndrome using radiomics and deep learning
Eline A van Lange1, Elisabeth J Bijl1, Cecile C de Vos1
1Department of Anaesthesiology, Center for Pain Medicine, Erasmus MC, Rotterdam, the Netherlands.
Introduction:
Complex regional pain syndrome (CRPS) is a complication after trauma or surgery. Skin temperature asymmetry is one of the few symptoms that can be measured, for which thermography can be used. However, the current assessment of thermograms relies on manual interpretation and is subjective.
Objective:
The aim of this study was to develop an automatic model for quantitative assessment of CRPS based on thermography by using deep learning and radiomics.
Methods:
In this study, 178 thermograms of the extremities of 98 patients with CRPS and 837 thermograms from 56 healthy controls were included. A deep learning model was developed to segment the extremities. From each thermogram, for each extremity, 564 radiomics features were extracted. Based on these features, a classification model was developed using a combination of machine learning approaches and evaluated through a 20x random-split cross-validation. The performance of the classification model was compared with visual scoring of the thermograms by 3 clinicians.
Results:
The radiomics classification model statistically significantly outperformed all 3 clinicians (mean area under the curve of 0.93 compared with 0.82, 0.79, and 0.69 [P < 0.001]).
Conclusion:
Our automatic classification model can distinguish thermograms of patients with CRPS from those of healthy controls with a performance that exceeds that of clinicians, thereby providing a basis for future studies evaluating its role in diagnosis and treatment monitoring.
