Machine learning and artificial intelligence can help medical device innovators improve
outcomes and build better systems. Unfortunately, clinical data is often in short supply or
in a form difficult to use for training. Using physics-based simulation, we train AI and ML
systems before using clinical data sets to maximize the algorithm’s effectiveness and to
make maximum use of any available clinical data. The result is robust algorithms with
minimal additional data collection, and fielding systems faster that help patients more
Simulation-based training cuts significant times off clinical and pre-clinical data collection, potentially saving years of effort and millions in clinical studies.
Our testing laboratory has been developed from the ground up to measure the important aspects of energy-tissue interaction. Using bench tissue or gel models we can collect the key material properties for your application as well as measure results to compare to the simulation predictions.
More Complete Training
By training your model on a parameter space that is bigger than the patient
population there is more complete coverage of special cases and edge conditions.
Simulation-Powered Artificial Intelligence and Machine Learning
Physics-based simulation provides realistic results that match the real world data coming from clinical trials. Traditional data science methods artificially expand data sets by adding random variation to parameters and results leading to training sets that contain configurations that are not accurate and do not reflect real world conditions.
Simulation-based data sets can be configured to provide results that cover the parameter space needed without the need to settle for whatever happens to be available clinically. Data set formats are easy to configure for machine learning training, providing the necessary information in a compact package for fast processing.
Through automated scripting and scalable cloud-based computation resources, simulation solutions can generate much larger data sets than can be collected through clinical, pre-clinical, or bench sources. This results in more robust algorithms, better coverage of the edge cases of the patent population, and an innovative product that can help more people.