FlowView Diagnostics offers a Cloud-based Software as a Service (SaaS) platform – asudes.
• manual identification of cell populations of interest
• operator dependent
• high dimensionality of data
• multiple markers
• multiple lasers
• manual decision steps
• experienced based job
• human biased and errors
• manual analysis of homogeneous cell population
• subjective but critical to correct findings
• technological evolution FC devices
We are committed to leveraging the power of technology and innovation in the field of flow cytometry, aiming to elevate patient care and drive efficiency in the healthcare landscape. Our focus is on the interpretation of complex data to positively impact patient outcomes, while simultaneously working to reduce the overall costs.
It empowers healthcare professionals with easy navigation through flow cytometry complexities for data-driven decisions. For researchers and CROs, it accelerates scientific discovery.
Powered by AI technology, asudes delivers unparalleled insights by deciphering complex flow cytometry data sets. It offers transparent and actionable recommendations, ensuring that data remains data, insights remain insights, and decisions become valuable.
Asudes empowers healthcare professionals to make precise and well-informed decisions. By reducing variabilities in outcomes, it aids in elevating the standard of patient care.
We recognize the importance of time in healthcare. Asudes significantly reduces processing time, allowing care teams to focus more on patients and less on data crunching.
For research organizations and CROs, asudes contributes to the advancement of medical knowledge by simplifying data analysis and facilitating research in various fields.
Eclipse algorithm is symplifying super-sized data. Embedded in our asudes platform, eclipse analyses the large volumes of raw data produced by MFC and filters out all normal cells, leading to a non-crowded representation of abnormal cells, shown as simplified 2D image.
Apply Simultaneous Component Analysis (SCA) to data from healthy patient samples for a robust Control Model, a key reference for normal cellular behavior.
Project the patient's blood sample cell data onto the established Control Model, identifying deviations for a comprehensive view of the cellular landscape.
Eclipse algorithm is employed to eliminate the patient's healthy cells, revealing the remaining cells in a two-dimensional image.
Principal Component Analysis (PCA) is utilized to identify and quantify the remaining abnormal cells and their interrelations.
The obtained analysis results are readily interpretable, providing clear indications of the necessity and urgency of treatment.