Authors : Mitali Saigal; Shilpa Mahajan; Anvessha Katti; Danish Raza Rizvi

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/zsyjybww

DOI : https://doi.org/10.38124/ijisrt/25may1869

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Abstract : Applications of AI and ML are expanding, exponentially in critical medical diagnosis. Deep learning has proven its prominence in population health. It has a distinct role in disease risk prediction. Both AI and ML are enhancing recognition of out-of-hospital cardiac arrests. Wireless Sensor Networks (WSNs) and IOT (Internet of things) are being increasingly integrated in health vitals monitoring in critical medical emergencies. Use of wireless sensor networks (WSN) with computational capabilities to monitor carbon dioxide levels (capnography) and hypoxia during per-operative general anesthesia while using an anesthesia machine in delivering mixture of anesthesia gases in critical surgeries can prevent medical emergencies. Wireless Medical Sensors are brought in intimate contact with body to collect critical physiological data. Modern Continuous anesthesia delivery machine administers a mixture of anesthetic gases. Real time monitoring of developing hypoxia with EEG, febrile condition, Blood pressure, rate of heart beat, convoluted carbon dioxide as an indicator of depth of anesthesia can be of immense use to help anesthesiologists plan a safer anesthesia. In case of a cardiac arrest, CO2 produced at the level of tissues fails to get transported to lungs. Here is where the elimination of CO2 takes occurs. In cardiopulmonary resuscitation estimation of that is produced in exhalation can be a guide in circulation assessment. In comparison to an electrocardiogram, blood pressure or pulse it serves better in diagnosis. It is generally Normoxia in a cardiac surgery decreases kidney injury. A sensors that is an attachment in a patient's body to gather patient's physiological data followed by concurrent wireless transmission of such data can be on circulated onto a Physicians portable device. In this publication, we make an attempt to achieve more insights into recent developments in Critical Medicine in particular applications focused on` how WSN and IOT can help maintain balanced Anesthesia with minimum cardiac arrest risk using a real time per-operative monitoring technique targeted towards hypoxia and end tidal carbon dioxide levels in blood.

Keywords : Capnography, Artificial Intelligence, Anesthesia, Cardiac Arrest, Internet of Things.

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