Anesthai Safe Dose: Smart Anesthesia System

Dr. Sowbahgya M P1, Mohammed Shazan 2, Mohan Gowda V C3 ,Puneeth S V4

                1Associate Professor                                                                                          2Student

Department of Computer Science and Engineering,                           Department of Computer Science and Engineering,

K.S. Institute of Technology,                                                                     K.S. Institute of Technology,

          Visvesvaraya Technological University, Belagavi - 590018. Bengaluru, Karnataka - 560109, India.

sowbhagya.mp@gmail.com   mohammedshazan888@gmail.com

3Student                                                                                                      4Student

Department of Computer Science and Engineering,                            Department of Computer Science and Engineering,

K.S. Institute of Technology,                                                                      K.S. Institute of Technology,

     Visvesvaraya Technological University, Belagavi - 590018. Bengaluru, Karnataka - 560109, India.

mohangowda4983@gmail.com                                                                   punithsv948@gmail.com

ABSTRACT-Anesthesia administration is a critical part of surgical procedures and requires accurate dosage control to ensure patient safety and successful surgical outcomes. Traditional anesthesia management mainly depends on manual calculations, continuous observation, and the experience of anesthesiologists. However, this approach may sometimes lead to dosage inaccuracies due to human error, workload pressure, delayed response, and differences in patient conditions such as age, weight, medical history, and physiological status. Incorrect anesthesia dosage can result in serious complications including respiratory depression, cardiovascular instability, prolonged recovery, or insufficient anesthesia during surgery. Therefore, there is a growing need for intelligent systems that can assist doctors in making safer and more accurate anesthesia-related decisions.To overcome these challenges, the proposed system, AnesthAI SafeDose, is developed as an intelligent anesthesia dose calculation and safety support system that combines artificial intelligence, real-time patient monitoring, and predictive analytics. The system analyzes patient-specific parameters such as age, weight, body mass index, medical history, ECG, heart rate, oxygen saturation (SpO₂), and other physiological signals to recommend an optimal anesthesia dosage. In addition, the system continuously monitors the patient during surgery and generates alerts whenever abnormal conditions or unsafe dosage levels are detected. By integrating machine learning techniques with real-time monitoring and clinical decision support, AnesthAI SafeDose reduces manual errors, supports anesthesiologists in critical decision-making, and improves the overall safety and efficiency of anesthesia management in modern healthcare systems.

KEY WORDS: Anesthesia, Dose Prediction, Machine Learning, Patient Safety, Clinical Decision Support System, Real-Time Monitoring