Artificial intelligence (AI) and mechanical imaging (MI) have been used in separate studies in breast imaging. They have individually shown great possibilities within the field of mammography, but the use of the two techniques together have never been evaluated. The artificial intelligence application used in this work was Transpara, a deep learning convolutional neural network. It distinguishes patterns in the mammographic images and provides scores of individual findings and the whole mammographic examination, which indicates a level of suspicion for breast cancer. Mechanical imaging is a surface stress measurement, that provides information of the mechanical structure of the underlying tissue.Since malignant tumours often express a higher relative pressure compared to the surrounding tissue in the breast, mechanical imaging is comparable with palpation but could provide even more information of the mechanical structures.The purpose of this work was to study if the combination of the two methods could be used to directly detect breast cancer. Screening images of 118 women were analysed in Transpara, and the pressure distribution measurement of the same women was obtained from a previous study on MI. For 46 cases, there was compression pressure present over the AI-findings, and these were chosen to be included in the analysis. Locations of findings with the highest level of suspicion and the corresponding locations in the pressure measurement were used to calculate the mean relative pressure over a finding. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. The increased pressure was then compared among these three groups and the two groups of cancer and healthy, to evaluate if the increased pressure over Transpara scores of women diagnosed with cancer was different from those diagnosed as healthy. The correlation between increased pressure and Transpara score was evaluated for each group, to evaluate if the two methods found the same indications for breast cancer.The results of this study indicated that there probably are differences in increased pressure between cases with breast cancer and healthy, but it remains to further evaluated for a larger material. A significant and relatively strong correlation between the relative pressure increase over an AI-finding and the Transpara scores was established in the group with cancer, but the other groups showed no correlation.This study indicates that MI combined with AI can potentially be used to improve the performance of mammography screening. It suggests that AI and MI find independent markers that coincide in breast cancer. Therefore, the two methods have the potential of lowering the recall rate in mammography, but this needs to be further evaluated.