Advancements in the Application of Multimodal Monitoring and Machine Learning for the Development of Personalized Therapeutic Strategies in Traumatic Brain Injury

Zhijing WeiLingda MengWei Chong^*

• The First Affiliated Hospital of China Medical University, Shenyang, China

Trauma is the fourth leading cause of death globally and the primary cause of mortality in the 15–45 age group, with traumatic brain injury (TBI) at the core of trauma care. Annually, over 50 million TBI patients are reported worldwide. The complex and heterogeneous pathophysiology of TBI presents substantial diagnostic and therapeutic challenges. In recent years, multimodal monitoring has emerged as a crucial tool to guide clinical management. The integration of multimodal monitoring with machine learning offers novel opportunities for TBI assessment and management, given the rapid development and widespread application of machine learning approaches. Therapeutic hypothermia has shown potential neuroprotective benefits in experimental and clinical contexts, though evidence remains mixed and its implementation in practice faces significant challenges. This review summarizes recent advancements in multimodal monitoring and explores how machine learning can optimize the application of therapeutic hypothermia in conjunction with multimodal data. For example, predictive models trained on multimodal signals (e.g., EEG, ICP, cerebral blood flow, and oxygenation) can help identify patient subgroups most likely to benefit from targeted temperature management. By enabling such stratification and adaptive treatment strategies, machine learning may support the development of more personalized and effective therapeutic approaches for TBI.