Clinical trials

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.

This study compares the automated anesthesia systems for painless abortion (AAS-PA) with traditional anesthesia in painless abortions. It assesses intraoperative indicators (hypoxemia, injection pain, hypotension, hemodynamics, respiratory depression) and postoperative outcomes (nausea, dizziness, recovery time, satisfaction). The AAS-PA group uses AI-adjusted intravenous ciprofol based on real-time vital signs and sedation depth; the traditional group relies on manual anesthesia. The goal is to verify if AAS-PA safely manages sedation, meets painless standards, reduces adverse events, and improves experience, aiding optimized automated protocols.

The goal of this clinical trial is to compare the performance difference between trained model and clinical professionals in anesthesia control during gastrointestinal endoscopy. The participants would include adults aged between 18-65 years old with ASA scores I-II. The main questions it aims to answer are: * Can AI model successfully and safely perform anesthesia during gastrointestinal endoscopy? * Researchers hypothesis that anesthesia performed by professionals is not inferior to those performed by AI model. The researchers will compare the results of the AI group with the traditional group to see if both groups have similar success rates in performing anesthesia. Participants will fast for 8 hours before the procedure, and drinking water will be prohibited for 2 hours after taking the intestinal purgatives.