Objective:

To consolidate and critically analyze contemporary machine learning and deep learning methodologies for the detection and classification of intracranial hemorrhage (ICH) from non-contrast CT scans, emphasizing the clinical significance of accurate diagnosis.

Approach:

Key Findings:

• Deep learning architectures show improved sensitivity and specificity for ICH subtype classification, with some studies reporting up to X% accuracy.
• Hybrid and transformer-based models enhance feature representation capabilities, as evidenced by recent benchmarks.
• Preprocessing techniques and explainability methods contribute to model robustness and clinical interpretability, addressing specific challenges in deployment.

Interpretation:

Machine learning and deep learning models have significant potential for automated ICH detection and classification, but challenges such as generalizability, dataset heterogeneity, and the need for clinical validation remain critical.

Limitations:

• Dataset heterogeneity and class imbalance affect model performance, leading to potential biases.
• Inconsistent expert labeling and variability in CT acquisition settings pose challenges that can undermine model reliability.
• Limited focus on conventional ML techniques in existing reviews restricts a comprehensive understanding of the field.

Conclusion:

Future research should prioritize large-scale multi-center validation, model interpretability, and integration into clinical workflows to enhance the practical deployment of automated ICH detection, addressing the urgent need for reliable diagnostic tools.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.