Can Machine Learning Be a Reliable Tool for Predicting Hematoma Progression Following Traumatic Brain Injury? a Systematic Review and Meta-Analysis Publisher



Mohammadzadeh I¹ ; Hajikarimloo B² ; Eini P³ ; Niroomand B¹ ; Mohammadzadeh S¹ ; Habibi MA⁴ ; Babak ZMSE⁵ ; Aliaghaei A⁶ Show Affiliations
Source: Neuroradiology Published:2025

Abstract

Background: Predicting hematoma progression in traumatic brain injury (TBI) is crucial for timely interventions and effective clinical management, as unchecked hematoma growth can lead to rapid neurological deterioration, increased intracranial pressure, and poor patient outcomes. Accurate risk assessment enables proactive therapeutic strategies, minimizing secondary brain damage and improving survival rates. Methods: This study evaluated to assess the performance of artificial intelligence (AI) algorithms, including machine learning (ML) and deep learning (DL), in forecasting risk of hematoma progression. Comprehensive searches across Embase, Scopus, Web of Science and PubMed identified relevant studies, with data extracted on algorithm metrics such as sensitivity, specificity, and area under the curve (AUC). Results: 1,240 studies screened, five out of them met the inclusion criteria, evaluating various AI models. The meta-analysis revealed a pooled sensitivity and specificity was 0.76 [95% CI: 0.67–0.83], 0.84 [95% CI: 0.78–0.89], positive and negative likelihood ratio was 4.82 [95% CI: 3.51–6.61] 0.29 [95% CI: 0.21–0.39], diagnostic score was 2.82 [95% CI: 2.33–3.32], diagnostic odds ratio was16.85 [95% CI: 10.29–27.59] and an AUC of 0.88 [95% CI: 0.85–0.90]. Among the evaluated algorithms, XGBoost has the best predictive performance with an accuracy of 91%. Integrating radiomics and clinical features in these models considerably improved the predictive outcomes. Conclusion: The current results demonstrated the potential of AI-based models to improve hematoma progression prediction for TBI patients, thereby supporting more effective clinical decision-making. Further research should aim to standardize datasets and diversify patient populations to improve model applicability and reliability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.