At the digital department of IM Sechenov First Moscow State Medical University, a team at the Ministry of Health of the Russian Federation has developed a neural network designed to analyze electrocardiograms (ECGs) and automatically allocate them to diagnostic classes with high precision. In practical terms, the system helps identify multiple cardiovascular conditions within a single patient and flags potential concerns for the treating physician. This advancement was discussed with socialbites.ca and Sechenov University researchers provided the explanation.
Timely and accurate interpretation of ECG data is essential in diagnosing heart disease. The challenge often lies in handling large volumes of readings and the uneven distribution of highly qualified cardiology expertise across regions. The new model is intended to streamline workflow by efficiently processing extensive ECG datasets and presenting clear, actionable classifications to clinicians.
Project leaders emphasize that ECG classification can categorize recordings by specific features, such as types of arrhythmia or ischemic changes. This capability is expected to reduce the time clinicians spend sifting through data and enable quicker, more informed decisions. As explained by the project manager, Alina Kitieva, a student at the digital department, the system is primarily designed to support medical decision-making rather than replace clinician judgment.
In practice, the workflow involves clinicians uploading patient ECG data into a secure program. The software analyzes the traces, assigns them to clinically meaningful categories, and then surfaces the results for review by a specialist who determines the next steps in care. The aim is a workflow where automated classification complements physician expertise, enhancing accuracy and speed in cardiovascular assessment.
The anticipated outcome is a model capable of classifying ECGs with at least 95 percent accuracy. Looking ahead, developers plan to broaden the repertoire of detectable cardiac conditions, refining the model and extending its application to medical institutions as a decision-support tool for clinicians. This effort mirrors a broader trend toward data-driven cardiology, where machine intelligence helps clinicians manage large, complex datasets more effectively.
Earlier work at Sechenov University included the creation of a virtual reality application for autism treatment in children, illustrating the institution’s ongoing interest in leveraging technology to advance patient care across diverse specialties.