AI-Assisted Precision Hyperbaric Oxygen Therapy Based on Dynamic BIS-EEG Feedback for Patients With Disorders of Consciousness

Disorders of consciousness (DoC) are severe neurological conditions characterized by impaired awareness and reduced interaction with the environment. Although hyperbaric oxygen therapy (HBOT) may promote neurological recovery, the optimal treatment strategy for individual patients remains unclear. This study aims to develop and validate an artificial intelligence-assisted precision decision support system for HBOT based on dynamic BIS-EEG brain functional feedback. The system will integrate clinical information, neuroimaging data, physiological parameters, consciousness assessments, and HBOT treatment characteristics. Continuous BIS-EEG monitoring will be performed during different phases of HBOT, including compression, oxygen exposure, air breaks, decompression, and post-treatment recovery. Artificial intelligence models will analyze these dynamic brain functional responses to predict treatment effectiveness and support personalized HBOT management. The primary outcome will be improvement in consciousness assessed by the Coma Recovery Scale-Revised (CRS-R). Secondary outcomes include changes in consciousness state, cognitive and functional recovery, and treatment-related adverse events.