Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.