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Abstract in Russian«CORINTEL.TECH»: artificial intelligence for electrocardiogram annotation DOI: 10.29188/2712-9217-2025-11-2-14-18
- Antipova A.A. – 5th-year student of the Faculty of Medical Biochemistry, student of the Digital Department 2024-2025, Project Leader of CORINTEL.TECH, I.M. Sechenov First Moscow State Medical University (Sechenov University); Moscow, Russia
- Dolmatova S.A. – student of the Faculty of General Medicine, student of the Digital Department 2024-2025, Donetsk National Medical University; Donetsk, DPR
- Volkova D.A. – student of the Faculty of Civilian Medical (Pharmaceutical) Specialists, student of the Digital Department 2024-2025, S.M. Kirov Military Medical Academy; Saint Petersburg, Russia
- Khatsiev R.T. – student of the «Applied Mathematics and Informatics» program, Faculty of the Academy of Engineering of RUDN University; Moscow, Russia
- Yaroshenko A.V. – postgraduate student, Academy of Engineer- ing of RUDN University; Bachelor's and Master's degrees from the MIPT; Employee of CARDIOTECH LLC; Moscow, Russia
- Andrikov D.A. – PhD (Eng.), engineer, scientific supervisor of the CORINTEL.TECH project, Associate Professor of the Department of Information Technologies and Medical Data Processing, I.M. Sechenov First Moscow State Medical University (Sechenov University); Moscow, Russia
7 The «CORINTEL.TECH» project represents an artificial intelligence (AI)–based software solution for the analysis and annotation of 12-lead electrocardiograms (ECGs).
The primary objective of the project is to enhance diagnostic speed and accuracy, optimize healthcare resource utilization, and provide an educational tool for medical professionals.
The development is aimed at addressing the national healthcare priority «Combating cardiovascular diseases» within the framework of the Russian national project «Healthcare».
The system employs a hybrid architecture that combines a convolutional neural network (CNN) with an attention mechanism for diagnostic feature extraction and a large language model (LLM) for generating a comprehensive textual interpretation.
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