Browsing by Author "Tomasevic, Smiljana (57430908700)"

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management. © 2024 IEEE.

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management. © 2024 IEEE.

Detection of clinically relevant stenosis within coronary arteries as well as planning of treatment (stent implantation) are important topics in clinical cardiology. In this study a thorough methodology for virtual stenting assistance is proposed, that includes the 3D reconstruction of a patient-specific coronary artery from X-ray angiography images, hemodynamic simulations of blood flow, computation of a fractional flow reserve (FFR) equivalent, virtual stenting procedure and an optimization of the virtual stenting, by considering not only the value of computed FFR, but also the low and high WSS regions and the state of arterial wall after stenting. The evaluation of the proposed methodology is performed in two ways: the calculated values of FFR are compared with clinically measured values; and the results obtained for automated optimized virtual stenting are compared with virtual stenting performed manually by an expert clinician for the whole considered dataset. The agreement of the results in almost all cases demonstrates the accuracy of the proposed approach, and the small discrepancies only show the capabilities and benefits this approach can offer. The automated optimized virtual stenting technique can provide information about the most optimal stent position that ensures the maximum achievable FFR, while also considering the distribution of WSS and the state of arterial wall. The proposed methodology and developed software can therefore be used as a noninvasive method for planning of optimal patient-specific treatment strategies before invasive procedures and thus help to improve the clinical outcome of interventions and provide better treatment planning adapted to the particular patient. © 2024 Elsevier Ltd

Detection of clinically relevant stenosis within coronary arteries as well as planning of treatment (stent implantation) are important topics in clinical cardiology. In this study a thorough methodology for virtual stenting assistance is proposed, that includes the 3D reconstruction of a patient-specific coronary artery from X-ray angiography images, hemodynamic simulations of blood flow, computation of a fractional flow reserve (FFR) equivalent, virtual stenting procedure and an optimization of the virtual stenting, by considering not only the value of computed FFR, but also the low and high WSS regions and the state of arterial wall after stenting. The evaluation of the proposed methodology is performed in two ways: the calculated values of FFR are compared with clinically measured values; and the results obtained for automated optimized virtual stenting are compared with virtual stenting performed manually by an expert clinician for the whole considered dataset. The agreement of the results in almost all cases demonstrates the accuracy of the proposed approach, and the small discrepancies only show the capabilities and benefits this approach can offer. The automated optimized virtual stenting technique can provide information about the most optimal stent position that ensures the maximum achievable FFR, while also considering the distribution of WSS and the state of arterial wall. The proposed methodology and developed software can therefore be used as a noninvasive method for planning of optimal patient-specific treatment strategies before invasive procedures and thus help to improve the clinical outcome of interventions and provide better treatment planning adapted to the particular patient. © 2024 Elsevier Ltd