AI-Assisted Precision Hyperbaric Oxygen Therapy Based on Dynamic BIS-EEG Feedback for Patients With Disorders of Consciousness
brief summary
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.
detailed description
This prospective randomized controlled clinical study will evaluate whether an artificial intelligence-assisted precision hyperbaric oxygen therapy strategy improves clinical outcomes compared with conventional hyperbaric oxygen therapy in patients with disorders of consciousness.
Patients with disorders of consciousness, including vegetative state/unresponsive wakefulness syndrome and minimally conscious state, will receive standardized hyperbaric oxygen therapy. Multimodal clinical data will be collected, including demographic characteristics, disease etiology, disease duration, baseline consciousness assessment, neuroimaging findings, physiological parameters, BIS-EEG signals, and HBOT treatment parameters.
Dynamic BIS-EEG monitoring will be conducted throughout the entire HBOT session. Brain functional responses will be evaluated during the following phases: pre-treatment baseline, compression, hyperbaric oxygen exposure, air-break interval, repeated oxygen exposure, decompression, and post-treatment recovery.
The artificial intelligence system will integrate multimodal information using machine learning approaches to identify predictive biomarkers of neurological recovery and provide individualized decision support regarding HBOT treatment parameters, including treatment pressure, oxygen exposure duration, air-break strategy, treatment frequency, and treatment course.
The effectiveness of the AI-assisted decision support system will be evaluated by comparing predicted treatment responses with clinical outcomes. Clinical outcomes will include changes in Coma Recovery Scale-Revised (CRS-R) scores, consciousness state transitions, cognitive and functional recovery, and adverse events associated with HBOT.
This study is expected to provide an objective and personalized approach for optimizing HBOT management in patients with disorders of consciousness.
official title
A Randomized Controlled Trial of Artificial Intelligence-Assisted Precision Hyperbaric Oxygen Therapy Based on Dynamic BIS-EEG Feedback in Patients With Disorders of Consciousness