How Does AI Technology Impact Healthcare Operations?


We Monitor Depth of ANESTHESIA


Most Accurate AI-Driven Technology, High Accuracy Predictions‎

Fast, Acurate & comfort

Some patients may require less anesthesia

We propose a novel feature of extracting the signals from electroencephalogram (EEG), cleaning and converting them through a DoA Feature Extractor, and along with machine learning processor in combination with EMG signal reduction, establish a relationship between input variables and an output which could automatically calculate and adjust the amount of Anesthesia infused on a real-time basis

Closed-loop Monitoring

The principles of closed-loop monitoring can be used in regional anesthesia, general anesthesia, monitored anesthesia care, and the intensive care unit.

Artificial Intelligence

Our goal in achieving these objectives are facilitated by extensive use of Artificial Intelligence, through the sub set of machine learning.

Machine Learning

Machine Learning involving (Deep learning and predictive analysis) will play a major role as they will aid in the integration of numerous and complicated inputs

EEG-Based Monitoring Could, Directly Monitor the Neurological Response to Anesthetic Agents.

Benefits of our Solution

Assists the preoperative assessment
This includes a thorough surgical overview, medical history, physical exam, lab tests, and identification of specific cardiac and pulmonary risk factors, with the goal of reducing perioperative risks and improving outcomes. This is a crucial aspect of presurgical care that has evidence-based prognostic consequences. For example, postoperative lung complications can be predicted by pre-existing chronic lung disease, severe asthma, smoking status, and other relevant characteristics, allowing physicians to stratify patients into risk levels. Subsequently, anesthesiologists may opt to modify their anesthetic choice and dosage, or perhaps attempt to optimize the condition of the patient before proceeding with the surgery according to the characteristics obtained in the preoperative assessment.
Transfer of workload
The process of knowledge integration for risk stratification was traditionally the sole responsibility of the physician. Machine learning will make this task accurate, efficient and timely.
Consolidation of functions
AI may assist the physician in higher order knowledge integration with the experience of thousands of medical procedures that a single person would not be able to integrate alone. AI also plays a crucial role in validating the robustness of its own outputs. In having access to limitless medical case studies, programs can be cross trained from various data sets to test predictive accuracy.
Bottom up approach
AI comes with the promise of self-sufficient and adaptable systems that can teach themselves through a bottom up approach, one that is pre-emptively given medical information from previous surgeries and real-time data about the patient to form a perception and malleable output that makes sense according to prior evidence and the current condition of the patient.
Fine tuning
Through the course of a procedure, an AI-based closed loop system would make granular adjustments to the administered anesthetic in real-time according to changes in the DoA measured by the equipment, and addition of new drugs. This type of AI would prove to be most efficient for an anesthesiologist who would now be able to monitor other key aspects of the patient’s anesthetic condition during surgery.
Total Reliability

The preoperative assessment program is enhanced by ML, fed through retrospective data from previous surgeries, not only regarding the type of surgery and patient risk factors, but also the clinical decisions made by the anesthesiologist and the post-operative functional outcomes. It could teach itself to make data-driven clinical recommendations concerning what anesthetics or other interventions would ensure the best outcome based on recent data from a vast array of similar cases. This form of bottom up processing where various inputs is used to form a reliable perception and opinion of conditions is the cornerstone of our proposed product offering.

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We intend to employ a closed loop where a processed EEG monitor is controlling the extent of anesthetics to be administered to the patient.

Office Address

Spark Centre Head Office
Suite 300,
2 Simcoe Street South,
L1H 8C1