AI-based digital twins trained with real-world patient generated data
Our technology builds digital twins of patients, closely mimicking their glycemic response through AI.
By adjusting factors like insulin dosage and meal timing, we simulate variations in blood glucose levels.
This patient-centric approach ensures personalized and effective support.
Simulation and prediction
Anticipate how changes in lifestyle or treatment plans might impact blood glucose control
Advanced data analysis
Pinpoint specific triggers or patterns that lead to high or low blood glucose levels
Personalized dose recommendation and tailored treatment plans that are more effective for a specific patient
Web app to boost HCPs efficiency
Utilizing a two-step approach: firstly, extracting behavioral patterns and therapeutic habits to comprehend the root causes of excursions, and subsequently, simulating to understand how to adapt habits or treatment for optimal glucose control.
Pinpoint therapeutic habits
ML algorithm for missing meal data: Insert missing meal data, carbohydrates and timing.
Automatic tagging of data: Distinguish meals from hypo-treatment and differentiate prandial bolus from correction.
Extraction of injection habits: Identify patterns in the timing between meals and bolus injections.
Extraction of correction trends: Analyze timing patterns between glycemic excursions and corrective actions.
Glycemic pattern analysis
Extraction of behavioral patterns contributing to glycemic excursions, where a pattern represents a sequence of actions leading to an excursion.
Highlight the most recurrent patterns aids in focusing on areas where the patient requires more assistance.
Currently under development
Tailored treatment plans
Not a classical simulator: Digital Twin allows to alter real-life glucose profile experienced by the patient, by adjusting specific components of his dataset, such as the bolus/basal dose and timing of injection, basal scheme, meal intakes...
Understand the impact of changes in habits or behavior on glucose control to facilitate personalized patient education.
Streamline the traditional lengthy, empirical trial-and-error treatment adaptation protocol and safely and efficiently adapt treatment plans by testing new strategies in-silico for improved patient outcomes.
Real-time blood glucose prediction
Integrated into a logbook app through our API, when the the patient doesn’t know the direction of his glucose levels or whether he is at risk of hypo or hyperglycemia, the digital twin can predict and offer personalized recommendations for preventive actions to avoid anticipated glycemic excursions.
This empowers the patient with new information, enabling them to make more informed decisions in anticipation of potential excursions.
Market-leading results from clinically validated AI technology
We rely solely on real-life patient data to build and test our technology. Our results are based on datasets gathered during observational trials we conducted in France or obtained from clinical trials shared by reference clinical and research centers in Europe and the US.
Prediction accuracy up to 99.9%
In observational trials, our prediction algorithms achieved market-leading accuracy: 99.9%, 98.6%, and 96.3% of computed blood glucose values within Parkes error grid A+B zones at 30-, 60-, and 90-minute horizons.
Prevent 85% of Glycemic Excursions
Our prediction engine stands out as a robust decision support tool. Physicians estimate that the predictive capability alone significantly enhances decision-making, preventing about 85% of anticipated glycemic excursions.
Up to 40% Faster
Ongoing trials aim to showcase the potential of our digital twin technology, demonstrating a 40% reduction in data analysis time for HCPs and could revolutionize the treatment adaptation process and therapeutic education.
CEO & Co-founder
Master’s degree from Technical University of Compiègne.
Worked as software engineer in a Californian mobile/web software start-up, and as sales manager and business unit director in a leading European consulting firm.
Fields of expertise: mobile app development, sales and business development.
Pr. Eric Renard
Clinical advisor (since 2017)
Head of the Department of Endocrinology, Diabetes and Nutrition, Lapeyronie University Hospital, Montpellier, France.
His main field of clinical research is focused on intensive insulin therapy aiming at physiological insulin delivery.
He devoted a large part of his work on developing insulin pump therapy and development of tools for continuous blood glucose monitoring.