FlowView Diagnostics

FlowView diagnostics: Asudes - a Clinical Decision Support Platform

FlowView Diagnostics B.V. – an innovative startup- is developing a clinical AI innovation named: Asudes*.

Asudes is a  Clinical Desicion Support Platform for Flow cytometry  that helps treating physicians, surgeons and general practitioners who want  to provide fast and consistent clinical feedback to enable treatment decisions. This by reducing variabilities in outcomes and reduction in processing time and therefore costs.

*commercially not yet available, expected in 2023

FLOWVIEW´S DECISION SUPPORT SYSTEM

Eclipse Algorithm

asudes algorithm

Asudes algorithm

FLOWVIEW DIAGNOSTICS

Multicolor FlowCytometry (MFC)

MultiColor Flow Cytometry is frequently used technology for diagnostics

01

What is Multicolour Flow Cytometry (MFC)

Flow cytometry (MFC) is a technology for analyzing cell characteristic with high sensitivity: it can detect up to one divergent cell amongst one million normal cells.

Multicolour Flow cytometry is a powerful tool, it has seen dramatic advances over the last 30 years, allowing unprecedented detail in studies of the immune system and other areas of cell biology advancements in this technique have led to the possibility of measuring dozens of characteristics per individual cell, providing large volumes of raw data.

MFC is a technology for analyzing cell characteristic with high sensitivity: it can detect up to one divergent cell amongst one million normal cells.

The crowded data output is currently manually interpreted by the expert for diagnosis and monitoring. Translating the data into relevant information requires lots of expertise, time, and experience. Moreover, the process is restricted to the availability of clinical expert and presents risks of human biases and errors.

02

What happens with collected data?

The crowded data output is currently manually interpreted by the expert for diagnosis and monitoring. Translating the data into relevant information requires lots of expertise, time, and experience. Moreover, the process is restricted to the availability of clinical expert and presents risks of human biases and errors.

03

Clinical Challenge

To tackle this clinical challenge, researchers of the Radboud University and the University Medical Centre Utrecht developed an algorithm that can analyze the large volumes of raw data produced by the MFC and show this in a simplified presentation, but also in 5D. The later makes it unique. The MFC device itself can make 2D images aswell. They however are not linked to reference patients or can be plotted in 4,4 or 5D.

The algorithm filters out all the normal cells, leading to a non-crowded representation of the abnormal cells only. Consequently, interpretation requires less expertise, less time and is trained to detect early indications of e.g. relapse.

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