Artificial Intelligence and Machine Learning in Anesthesia
Depth of Anesthesia monitors helps individualize anesthesia by permitting accurate drug administration against the measured state of arousal of the patient.
About Our Product
We are offering a package that will help Anesthesiologists to get the best anesthesia dosage. Our product is a “Processor” which will help Anesthetic agents to get more accurate results and get an accurate DoA. The Processor will include hardware and an application (AI).
How it Works
For accurate monitoring of DoA, novel feature extraction is used to convert the EEG signal and combine with EMG signal reduction. Our machine learning based processor is used to facilitate the Closed Loop system. There are bound to be redundancies in the current system used by our clients, to be replaced by our system. We expect the efficiencies will outweigh the price paid to implement our Processor.
Accurate Monitoring
Accurate assessment of the depth of anesthesia and accurate monitoring contributes to tailoring drug administration to the individual patient and it is essential for intraoperative and postoperative patient’s health. A patient could be underdosed or overdosed because of equipment failure or error. On the contrary, inappropriate titration of the hypnotic components, leading to an excessive Depth of Anesthesia (DoA), might compromise patient outcomes.
Better Drug Administration
Depth of Anesthesia monitors might help to individualize anesthesia by permitting accurate drug administration against the measured state of arousal of the patient. Various depth of anesthesia monitors based on processed analysis of the EEG or mid-latency auditory-evoked potentials are commercially available as measures of anesthetic drug effect. However, not all of them are validated to the same extent. Depth of anesthesia monitoring becomes more compelling if it can be used to guide the clinician to the “sweet spot” where the anesthetic dose is sufficient to prevent awareness but not greater than needed.
Our Product Specifications
- Ability to display and perform analysis and classification (all in Realtime mode).
- Ability to display multichannel EEG signals up to 8 channels.
- Ability to remove Baseline from EEG signals.
- Ability to extract and display different frequency band signals from the main signal.
- Ability to calculate PSD (Power Spectrum Density).
- Ability to calculate 95% SEM features, Beta Ration, and power of different frequency bands.
- Use fuzzy classifier and scoring system to detect DOA.
Our Competitive Advantages
- Currently no similar product in the market such as our processor.
- Our processor can calculate accurate DoA estimation irrespective of the patient’s age and anesthetic drug.
- For our hardware implementation of DOA estimation algorithms, will be programmed using VHDL.
- The processor has proved to be capable of achieving a 100% accuracy for all DoA states.
- The processor can be integrated into an existing setup, with minimal redundancies.
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.
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.
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.
What is Our Solution
Learn More About Our Product
We are 24/7 available
Contact Us We Love to Hear From You.
Office Address
Spark Centre Head Office
Suite 300,
2 Simcoe Street South,
Oshawa,
L1H 8C1
Canada