Pia Schneider | Searcharxiv

arXiv2022

Stress-induced hyperalgesia instead of analgesia in patients with chronic musculoskeletal pain

Many individuals with chronic musculoskeletal pain (CMP) show impairments in their pain-modulatory capacity. Although stress plays an important role in chronic pain, it is not known if stress-induced analgesia (SIA) is affected in patients with CMP. We investigated SIA in 22 patients with CMP and 18 pain-free participants. Pain thresholds, pain tolerance and suprathreshold pain ratings were examined before and after a cognitive stressor that typically induces pain reduction (SIA). Whereas the controls displayed a significant increase in pain threshold in response to the stressor, the patients with CMP showed no analgesia. In addition, increased pain intensity ratings after the stressor indicated hyperalgesia (SIH) in the patients with CMP compared to controls. An exploratory analysis showed no significant association of SIA or SIH with spatial pain extent. We did not observe significant changes in pain tolerance or pain unpleasantness ratings after the stressor in patients with CMP or controls. Our data suggest that altered stress-induced pain modulation is an important mechanism involved in CMP. Future studies need to clarify the psychobiological mechanisms of these stress-induced alterations in pain processing and determine the role of contributing factors such as early childhood trauma, catastrophizing, comorbidity with mental disorders and genetic predisposition.

q-bio.NC↗

arXiv2021

Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework

In this work we use the AUCMEDI-Framework to train a deep neural network to classify chest X-ray images as either normal or viral pneumonia. Stratified k-fold cross-validation with k=3 is used to generate the validation-set and 15% of the data are set aside for the evaluation of the models of the different folds and ensembles each. A random-forest ensemble as well as a Soft-Majority-Vote ensemble are built from the predictions of the different folds. Evaluation metrics (Classification-Report, macro f1-scores, Confusion-Matrices, ROC-Curves) of the individual folds and the ensembles show that the classifier works well. Finally Grad-CAM and LIME explainable artificial intelligence (XAI) algorithms are applied to visualize the image features that are most important for the prediction. For Grad-CAM the heatmaps of the three folds are furthermore averaged for all images in order to calculate a mean XAI-heatmap. As the heatmaps of the different folds for most images differ only slightly this averaging procedure works well. However, only medical professionals can evaluate the quality of the features marked by the XAI. A comparison of the evaluation metrics with metrics of standard procedures such as PCR would also be important. Further limitations are discussed.

cs.CV↗