Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study
Not Applicable
Recruiting
- Conditions
- Surgical cases undergoing general anesthesia
- Registration Number
- JPRN-UMIN000044894
- Lead Sponsor
- Yamagata University
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- All
- Target Recruitment
- 100
Inclusion Criteria
Not provided
Exclusion Criteria
Patients who have been sedated prior to induction of general anesthesia. Patients undergoing tracheal intubation prior to induction of general anesthesia. Patients with contraindications to propofol or remimazolam. Patients who did not give their consent to participate in the study. Patients with aortic aneurysms or cerebral aneurysms that require management to prevent excessive blood pressure fluctuations.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Accuracy of Deep Learning Model Using Biometric Images Before General Anesthesia Induction for Predicting Blood Pressure Decline after General Anesthesia Induction
- Secondary Outcome Measures
Name Time Method