BIS

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.

The aim of this observational study is to learn the effects of hormone therapy (testosterone) applied in micropenis hypospadias surgery on the depth of anesthesia under general anesthesia. The main question it aims to answer is: The effect of testosterone therapy on the depth of general anesthesia. Based on these, to comment on the depth of anesthesia of patients who have just started using testosterone for sex change. To follow the depth of anesthesia of patients who have undergone hypospadias surgery with BIS monitoring and to record respiratory parameters under general anesthesia.

Previous pilot study had demonstrated a correlation between cognitive reserve and BIS variability during steady state of anesthesia. This study, we intend to evaluate the association between BIS variability and emergence and recovery time from anesthesia.