The 2D synthetic image (SM) generated from digital breast tomosynthesis (DBT) has the potential to replace conventional digital mammography (DM), therefore reducing patient dose without affecting the cancer detection performance. In this work, we analysed the image quality of SMs from three different manufacturers for the specific task of detecting microcalcifications (MC), in comparison to DM. A phantom with MC clusters on a uniform background was employed, thus also allowing to explore its feasibility to be used for quality control (QC). A 4-Alternative Forced Choice (4AFC) experiment was performed by four human observers, for detection of MC clusters on a region-of-interest level. We also explored the possibility to replace human observers with a virtual observer. For this, we developed a deep learning convolutional neural network (CNN) for the task of classifying the same images from the 4AFC study, and then compare the results to the human-based study. The results showed that for the four readers and all the systems, the percentage of correct answers (PC) was 100% and the visibility was 3 for the largest MC clusters. However, SM yielded worse detectability than DM for MC with sizes between 180 and 100 μm (PC was around 18% inferior in average). The CNN yielded the same relative results across modalities and systems than the 4AFC study, but in terms of the area under the receiver operating characteristic curve. This might encourage the possibility to develop QC procedures based on artificial intelligence image reading, improving reproducibility and reducing costs.