A multi-faceted article describing various applications of AI in healthcare has been published by Austria Presse Agentur (DE).

encevis team members contributed by discussing our solution for automatised data analysis of large EEG datasets for fast and reliable epilepsy diagnosis in adult and pediatric population.

See here:

• “Gesund mit KI“
• “Wie KI die medizinische Diagnose und Therapie verbessert“
• “Was in vielen Labors und Kliniken schon Alltag ist“

A new article (DE) about our project was published on GesundheitWirtschaft.at platform.

The publication describes our new projects dedicated to the development of AI-powered algorithms for automatic detection of epileptic spikes and seizures in EEG data recorded from pediatric patients.

Follow the link to read more: https://www.gesundheitswirtschaft.at/blog/eine-schnellstmoegliche-epilepsiediagnose-bietet-betroffenen-kindern-eine-chance-fuer-bestmoegliche-entwicklung/

“Künstliche Intelligenz soll das Gehirn retten helfen” is the title of the article published on 9.December.2023 in Die Presse newspaper.
Hannes Perko from the encevis team, in interview with Gerald Stampfel from Die Presse, talked about our stroke-focused project, and how efficiency of patient triage can be improved with AI analysis of EEG data.
Digital version of the article can be also found on the website of Die Presse (DE).

Within the framework of newly funded FFG-project, encevis team is working on developing AI-powered algorithms specifically for children and adolescents with suspected or confirmed epilepsy.
Building on our existing expertise with automatic review and analysis of (long-term) EEG data recorded from adults, we are now striving towards extending our application by training and validating algorithms on the brain activity data of young patients.

Both healthy and pathological brain activity might look very different depending on age. The anatomical and functional architecture of the brain undergoes tremendous changes across the development. Some epileptic syndroms occur only (e.g. ESES = electrical status epilepticus in sleep) or predominantly (e.g. absence seizures) in young age. Moreover, epileptic discharges can negatively impact the maturation process, or even cause regressions. When training algorithms to automatically detect epileptic activity in EEG data of children, all these factors must be considered.

encevis team is very much excited to embrace this challange!
To learn more, read the article describing our new project on the AIT’s website in DE or EN.