IN SUPPORT OF NEUROLOGY
Our mission is to offer a platform for "bench-to-bedside" innovations in neurology, and bring AI into the clinical workflow.
Today, the standard of EEG analysis is by visual inspection. This process is time-consuming and error-prone. Moreover, obtaining a confident diagnosis is very much linked to the level of experience of the reviewing neurologist. Many people responsible for reading an EEG do not have formal training for doing so. Learning to read an EEG is not mandatory to become a neurologist.
enables EEG diagnostics, powered by machine learning and artificial intelligence, and designed for the clinical workflow.
Available as a fully certified medical device by May 2021.
Free of charge to all Swiss university hospitals and research collaborators.
OPPORTUNITY: Automated Analysis
According to a recent study, 10 out of 10 neurologists find it useful to have quantitative analysis methods assist them in the everyday interpretation of routine EEGs. A significant portion of conclusions based on visual interpretation alone are subsequently altered because of quantitative analysis.
OPPORTUNITY: Source Imaging
In research, source Imaging is the basis for many applications with proven benefits. But it is too time-consuming and too demanding to perform within the clinic. We offer a source imaging pipeline that is fast, reliable, and easy to use, such that it can be seamlessly integrated into routine clinical EEG analysis.
The first step in source imaging requires the recording of a routine low-density EEG.
To estimate the generators of the recorded EEG, it is combined with a head-MRI of the patient or, if not available, with an appropriate age-adjusted template. A wide selection of age-adjusted templates is part of our source imaging solution.
Source activity over time is visualized to give a qualitative overview of activation patterns. These time-dependent patterns build the basis for further analyses, e. g. estimations of functional or effective brain networks.
An example of such an analysis would be the estimation of effective connectivities, based on resting state EEG. Such networks can be used to measure disease progression, or predict cognitive decline, e. g. in Alzheimer's disease.
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We are located at Novartis Campus.