William C Becker

Using 178 thermograms from 98 patients with complex regional pain syndrome (CRPS) and 837 thermograms from 56 healthy controls, the study used a U-Net deep learning model to segment extremities automatically (mean Dice similarity coefficient 0.99), extracted 564 radiomics features per extremity per thermogram (intensity, shape, texture), and built a classification model with the WORC automated machine learning framework under 20x random-split cross-validation; the model distinguished CRPS from healthy control thermograms with a mean AUC of 0.93 (95% CI 0.90-0.97), statistically significantly better than three clinicians' 0.82, 0.79, and 0.69 (P < 0.001).

Using 178 thermograms from 98 patients with complex regional pain syndrome (CRPS) and 837 thermograms from 56 healthy controls, the study used a U-Net deep learning model to segment extremities automatically (mean Dice similarity coefficient 0.99), extracted 564 radiomics features per extremity per thermogram (intensity, shape, texture), and built a classification model with the WORC automated machine learning framework under 20x random-split cross-validation; the model distinguished CRPS from healthy control thermograms with a mean AUC of 0.93 (95% CI 0.90-0.97), statistically significantly better than three clinicians' 0.82, 0.79, and 0.69 (P < 0.001).