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Beta testing of Antshrike app is successfully completed.

Before Health Intelligence is showcasing its technologies at the world's largest consumer electronics exhibition in Las Vegas (https://www.ces.tech/), visit us at Eureka Park for CES 2025!

Cardiovascular disease (CVD) is the leading cause of death globally, affecting millions of people every year. The World Health Organization estimates this number at 17.9 million people.

Any algorithm is at most a simplified model of real-world phenomena. Therefore, it can never fully capture all possible factors that are critical to understand disease states. To this end, the outcomes of our algorithms shall be treated with care and cannot be considered by any means a perfect reflection of real clinical pictures.

An innovative app from Before Health Intelligence identifies early signs of cardiac issues using wearable device data

In general, the quality and accuracy of the output of an SaMD depends directly on the quality of input data. For our algorithms, two factors are primarily affecting the quality of calculated scores. Firstly, the data collected from smartwatches is not as accurate and precise as properly calibrated medical devices in clinical environments. Thus, the input data generally is less reliable and cannot replace clinical investigations. Secondly, if the smartwatches are not worn or worn properly, or issues with sensors, batteries etc of the watches are present, the algorithm will be exposed to missing data that will affect its efficacy.

The past decades have been witnessing a major trend towards evidence-based classification of psychiatric disorders using measurable dimensions. The EERLYY app was the result of a major effort to use continuous longitudinal measurements of biobehavioral variables to characterize substance use disorders in a quantitative manner and relapse as a transition between states.

The use of mobile applications to inform or even drive clinical management is often associated with inherent risks. We are continuously assessing these risk factors and urge clinicians and patients to inform us on issues associated with the use of our technologies.

This mobile application is alike all SaMDs subject to changes and modifications. In particular, we strive the improve the performance of our algorithms, make sure that the app continues working flawlessly with mobile phone updates and any existing errors are corrected. Please be aware of these changes and update the application whenever a new version becomes available.

In contrast to the existing approaches, the algorithms underlying Eerlyy learn from the individual behavior of patients and therefore provides a platform for clinicians to develop personalized treatment strategies.

We closed our seed funding round successfully.