About us

We design, develop and market new patented technologies in the depth of anesthesia monitoring field through a commitment to innovation and excellence in the field of perioperative patient care. Given that the intensity of surgical stimulation varies throughout surgery, and the hemodynamic effects of the anesthetic drugs may limit the amount that can be given safely, it is not uncommon for there to be critical imbalances between anesthetic requirement and anesthetic drug administration.

Our Mission

Our Mission Is to prevent excessively deep anesthesia, which may be associated with delayed emergence from anesthesia and increased risk of perioperative complications.

Our Vision

Tools like Electroencephalogram (EEG)-based depth monitoring add insight into anesthetic effect during Total Intravenous Anesthesia (TIVA). Electroencephalogram (EEG) is a surface recording of the summed cortical electrophysiological activity and is altered by the level of consciousness.

We use in Machine Learning are Neural Networks an Deep learning in order to program our “Processor” which helps Anesthetic agents to get more accurate results and get precise DoA real time, and simultaneously adjusts the input of the amount of Anesthesia through a closed end loop.

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 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 postoperative 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 input is used to form a reliable perception and opinion of conditions is the cornerstone of our proposed product offering.