At the digital department of the First Moscow State Medical University named after IM. Sechenov from the Ministry of Health of the Russian Federation created a neural network that can analyze ECGs and automatically distribute them to classes with high accuracy. In other words, the model will help to simultaneously detect various pathologies of the cardiovascular system in a patient and draw the doctor’s attention to this fact. socialbites.ca was told about this at Sechenov University.
Diagnosis of cardiovascular diseases requires rapid and accurate analysis of electrocardiograms; This can often be difficult due to the large amount of data and the lack of highly qualified experts in certain regions.
The developers are confident that automating the ECG classification process will make it easier for healthcare workers to work with large amounts of data and help in the timely diagnosis of cardiovascular diseases.
“ECG classification allows you to divide recordings according to certain characteristics, for example, arrhythmias and type of ischemia, and this will significantly facilitate the work of cardiologists and therapists. This means that our model can find multiple pathologies at the same time and the doctor can confirm or deny their presence. Project manager Alina Kitieva, a student of the digital department of the First Moscow State Medical University, explained to socialbites.ca: The system is primarily aimed at supporting medical decision-making.
The use of the new digital system looks like this: A doctor uploads patients’ ECG data into a program, which then analyzes and distributes them to classes, presenting the results to a specialist for further analysis and decision-making.
The result of the project will be a model that can classify ECGs with at least 95% accuracy. In the future, the development team plans to expand the set of classified heart diseases in the model and apply the system in medical institutions to support medical decision-making.
Previously at Sechenov University was created VR application for autism treatment in children.
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Source: Gazeta
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