Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta‐Analysis

Deep learning models are highly effective in segmenting ICH on noncontrast computed tomography, demonstrating potential improvements in clinical neuroimaging. Despite their efficacy, challenges in segmenting smaller hemorrhages remain. The findings suggest that deep learning could reduce health care professional workloads and enhance patient care, although further research is needed to address limitations and extend clinical utility.