Deep Learning for Automated Intracranial Hemorrhage Detection in CT Imaging: A Narrative Literature Review

Abstract

Intracranial hemorrhage (ICH) is a time-sensitive neurological emergency in which rapid detection can influence triage, specialist consultation, and treatment planning. Recent deep learning research has substantially advanced automated ICH detection on non-contrast head computed tomography (CT), moving from slice-level convolutional neural networks to sequence-aware, three-dimensional, segmentation-driven, and workflow-integrated systems. This narrative literature review synthesizes representative studies on automated ICH detection, subtype classification, lesion segmentation, external validation, and clinical implementation. The review is anchored by Kar et al. (2024), which presented a deep learning framework for automated intracranial hemorrhage detection in medical image analysis, and situates that paper within a broader body of work spanning seminal benchmarks, clinically oriented validation studies, and recent systematic reviews. The literature shows that deep learning models can achieve strong diagnostic performance, especially when trained on large annotated datasets and designed to exploit volumetric context. However, real-world deployment remains limited by dataset shift, class imbalance, low-prevalence screening environments, limited prospective multicenter evaluation, false-positive burden, and insufficient interpretability and governance mechanisms. The review concludes that the next stage of research should prioritize robust external validation, clinically meaningful reporting, uncertainty estimation, workflow-aware evaluation, and hybrid pipelines that unify detection, subtype classification, segmentation, and outcome-oriented triage.

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