Use of Artificial Intelligence to Assess the Severity of Mitral Regurgitation
Yu. V. Goloshchapova,
D. V. Lukyanenko,
O. M. Meshcheryakova,
A. N. Katrich,
A. A. Khalafyan,
V. A. Akinshina,
O. V. Astafeva https://doi.org/10.35401/2541-9897-2026-11-1-16-23
Abstract
Objective: To develop high-accuracy neural network model for the diagnosis and prediction of the severity of mitral regurgitation as assessed by echocardiography.
Materials and methods: A total of 80 patients were divided into two groups: Group 1 included 42 patients with an eccentric mitral regurgitation jet, and Group 2 included 37 patients with a central mitral regurgitation jet. All patients underwent transthoracic echocardiography with assessment of mitral regurgitation severity based on the percentage ratio of the regurgitation jet area to the left atrial area. The vena contracta, proximal isovelocity surface area radius, and the magnitude of the horizontal color Doppler expansion artifact were measured. The effective regurgitant orifice area and mitral regurgitant volume were calculated.
Results. A neural network model was developed that predicts “moderate” and “severe” forms of mitral regurgitation based on echocardiography data with highest possible accuracy (100%). The accuracy for predicting the “mild” form was slightly lower (83.33%).
About the Authors
Yu. V. Goloshchapova
Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1
Russian Federation
Russian Federation
Yulia V. Goloshchapova - Diagnostic Ultrasound Physician, Department of Diagnostic Ultrasound, Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1.
167 1 Maya St., Krasnodar, 350086
D. V. Lukyanenko
Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1
Russian Federation
Russian Federation
Daria V. Lukyanenko - Diagnostic Ultrasound Physician, Department of Diagnostic Ultrasound, Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1.
Krasnodar
O. M. Meshcheryakova
Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1
Russian Federation
Russian Federation
Olga M. Meshcheryakova - Diagnostic Ultrasound Physician, Department of Diagnostic Ultrasound, Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1.
Krasnodar
A. N. Katrich
Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1; Kuban State Medical University
Russian Federation
Russian Federation
Aleksey N. Katrich - Dr. Sci. (Med.), Head of the Department of Diagnostic Ultrasound, Scientific Research Institute – Ochapovsky Regional Clinical Hospital No. 1.
Krasnodar
A. A. Khalafyan
Kuban State University
Russian Federation
Russian Federation
Alexan A. Khalafyan - Dr. Sci. (Tech), Professor, Department of Data Analysis and Artificial Intelligence, Kuban State University.
Krasnodar
V. A. Akinshina
Kuban State University
Russian Federation
Russian Federation
Vera A. Akinshina - Cand. Sci. (Ped.), Associate Professor, Department of Data Analysis and Artificial Intelligence, Kuban State University.
Krasnodar
O. V. Astafeva
Kuban State Medical University
Russian Federation
Russian Federation
Olga V. Astafeva - Dr. Sci (Med.), Professor of the Department of Radiation Diagnostics No. 1, Kuban State Medical University.
Krasnodar
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For citations:
Goloshchapova Yu.V., Lukyanenko D.V., Meshcheryakova O.M., Katrich A.N., Khalafyan A.A., Akinshina V.A., Astafeva O.V. Use of Artificial Intelligence to Assess the Severity of Mitral Regurgitation. Innovative Medicine of Kuban. 2026;11(1):16-23. (In Russ.) https://doi.org/10.35401/2541-9897-2026-11-1-16-23
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