Cerebral microbleeds have become the focus of a number of studies due to their potential as markers of disease burden, clinical outcomes and delayed effects of therapy, the authors wrote. However, manually detecting CMBs is time-consuming and traditional algorithms have fallen short in detection and labeling tasks.

Compared to other deep convolutional neural networks (DCNNs), Chen and colleagues, wrote the size of their dataset was still relatively small. More work lies ahead before DCNNs can become commonplace, they added, but further studies should include participants with more CMBs caused by other diseases.

“As in many applications of DCNNs, our 3D deep residual network approach can accurately detect CMBs, but the network itself lacks transparency,” Chen et al. concluded. “In order for DCNNs to be routinely adopted by clinicians, future exploration of their interpretation is necessary in order to provide a more concrete explanation for the rationale behind different network design strategies and increase confidence in their results.”

Results of the study were published online, Dec. 3 in the Journal of Digital Imaging.