Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid–structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set. A method for the design of a synthetic database of geometric models is presented in this study. We suggest using synthetic geometries that enable the development of several aortic valve and left ventricular models in a range of sizes and shapes. In particular, we developed 22 variations of left ventricular geometries, including one original model, seven models with varying wall thicknesses, seven models with varying heights, and seven models with varying shapes. To guarantee anatomical accuracy and physiologically acceptable fluid volumes, these models were verified using actual patient data. Numerical simulations of left ventricle contraction and aortic valve leaflet opening/closing were performed to evaluate the electro-physiological potential distribution in the left ventricle and wall shear stress distribution in aortic valve leaflets. The proposed synthetic database aims to increase the predictive power of machine-learning models in cardiovascular research and, eventually, improve patient outcomes after aortic valve surgery.

Background and Objectives: To propose a new classification of diabetic foot syndrome adapted for inclusion in the ICD-10 (the ICD-10 is the 10th revision of the International Statistical Classification of Diseases) and providing more reliable data on the number of clinical cases. Materials and Methods: A randomized controlled trial was performed. A total of 180 patients (36.6%) discharged from the hospital after surgical treatment and 312 patients (63.4%) who applied independently were observed and analysed. All patients had type 2 diabetes and were comparable in gender, age, duration of diabetes, area and nature of the wound defect. Results: We proposed to add the following to the existing ICD-10 and the emerging ICD-11 codes: Edf10.0—insulin-dependent diabetes mellitus with diabetic foot syndrome and Edf11.0—non-insulin-dependent diabetes mellitus with diabetic foot syndrome, where “df” is an acronym for diabetic foot. The new classification designates the seven most frequent areas of the lesion and five degrees of depth of soft tissue lesions. Conclusions: The proposed classification adapted for ICD-10 will enable the standardisation of diagnosis, providing a complete picture of this complication of diabetes mellitus, determining the number of amputations and their validity. Accurate statistics will allow for objective funding and timely preventive measures.

Transcatheter aortic valve implantation (TAVI) is a minimally invasive approach for the treatment of aortic stenosis in patients with a high risk of postoperative complica-tions who are not candidates for open surgery operation. Despite the rapidly growing number of such operations, they have a number of complications with limited choice of parameters and types of implants that do not take into account the individual characteris-tics of the patient. This leads to the question of customization of TAVI implants. The de-velopment of personalized prosthetic designs can reduce the incidence of postoperative complications, such as paravavular regurgitation or damage to the annulus fibrosus, thereby improving patients’ quality of life by optimizing hemodynamic indicators. Thus, the purpose of this study is to numerically simulate the hemodynamic characteristics in the aorta after TAVI to assess the impact of the prosthesis design on its effectiveness. This paper presents eight parametrized prosthesis geometries: two represent models of commercial valves (Evolut R PRO and SAPIEN 3), and the remaining six were developed by the authors. A coupled problem involving fluid-structure interaction was solved. A velocity profile based on echocardiography data was prescribed as the inlet boundary condition at the aorta, while a pressure determined by a two-element Windkessel model was prescribed at the outlet. Based on a comprehensive analysis of the integral parame-ters, three geometries were selected. These geometries demonstrate optimal hemody-namic performance: effective orifice areas exceeding 1.5 cm², moderate pressure gradi-ents (7–12 mmHg), and peak systolic velocities below 2 m/s. Additionally, they exhibit low OSI values (OSI < 0.25), which is indicative of a reduced risk of thrombosis and prema-ture leaflet fatigue. The obtained results provide a basis for the design, engineering, and manufacture of personalized TAVI prostheses that take into account the specific anatomi-cal and physiological features of each patient.

A new approach is proposed for evaluating the motion trajectories of aortic valve leaflets throughout the cardiac cycle based on echocardiography data, aimed at subsequently determining their mechanical properties. The non-invasive nature of this method presents a significant advantage over existing experimental techniques for assessing the mechanical properties of soft tissues, which often involve invasive procedures and are limited by sample availability and ethical considerations. In this study, a software module was developed to track the movement of control points marked on the aortic valve leaflet, using computer vision algorithms applied to echocardiography video data. The program utilizes a neural network based on the YOLOv8 library for semantic segmentation and object detection, enabling precise identification and real-time tracking of the valve leaflets. The software was initially validated on model data to ensure the accuracy and reliability of the tracking algorithm. It was then applied to real echocardiography data, successfully reconstructing the trajectories of the aortic valve leaflets throughout the cardiac cycle. These trajectories can be used in conjunction with mathematical models to determine the mechanical properties of the valve leaflets using optimization methods. The proposed method represents a non-invasive, efficient, and accurate means of assessing the dynamic behavior of aortic valve leaflets, potentially contributing to improved diagnosis and treatment planning for patients with aortic valve diseases. Further development of this approach may lead to a deeper understanding of the valve's mechanical characteristics and enhance surgical outcomes.

Предлагается новый подход для оценки траекторий движения створок аортального клапана в течение сердечного цикла на основе данных эхокардиографии, направленный на последующее определение их механических свойств. Неинвазивный характер этого метода представляет значительное преимущество перед существующими экспериментальными техниками оценки механических свойств мягких тканей, которые часто включают инвазивные процедуры и ограничены доступностью образцов и этическими соображениями. В данном исследовании разработан программный модуль для отслеживания движения контрольных точек, отмеченных на створке аортального клапана, с использованием алгоритмов компьютерного зрения, примененных к видеоданным эхокардиографии. Программа использует нейронную сеть на основе библиотеки YOLOv8 для семантической сегментации и обнаружения объектов, что позволяет точно идентифицировать и отслеживать створки клапана в реальном времени. Программное обеспечение было первоначально валидировано на модельных данных для обеспечения точности и надежности алгоритма отслеживания. Затем оно было применено к реальным данным эхокардиографии, успешно реконструируя траектории створок аортального клапана в течение сердечного цикла. Эти траектории могут быть использованы в сочетании с математическими моделями для определения механических свойств створок клапана с помощью методов оптимизации. Предлагаемый метод представляет собой неинвазивное эффективное и точное средство оценки динамического поведения створок аортального клапана, что потенциально способствует улучшению диагностики и планирования лечения пациентов с заболеваниями аортального клапана. Дальнейшее развитие этого подхода может привести к более глубокому пониманию механических характеристик клапана и улучшению результатов хирургических вмешательств.