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Description
Cardiovascular disease is the leading cause of death, globally. Approximately 42% are caused by Coronary Artery Disease . The aim of the project is to develop an efficient cloud-based solution for improving the clinical decision making process and correspondingly the long-term outcome for non-culprit lesions in atherosclerotic patients. The aim will be achieved by (i) Development of a cloud-based solution which integrates tools for assessing non-culprit lesions in routine clinical workflows , (ii) Development of a risk score specific for non-culprit lesions in atherosclerotic patients and (iii) Successful completion of the clinical trials planned during the course of the project. The innovative aspects of the proposed approach resides in the attempt to combine a multitude of medical imaging and non-imaging technologies to improve the clinical decision-making process for non-culprit lesion in atherosclerotic patients. This is achieved by additionally performing advanced analytics related to anatomical, functional and morphological assessment of lesions and associated plaque. Moreover, a lesion-specific risk score indicating the future risk of major adverse cardiac event provides valuable information for the clinical decision making process. At this point the goal is not to develop a method capable of providing directly the clinical decision, but rather present to the clinician the most relevant characteristics and prognostics, and, thus, to allow him to take a more informed decision. The project partners will accommodate the key needs of the proposal, under the considered topic. It is composed of a partner providing clinical expertise, and two partners with extensive expertise in development and validation of clinical decision support technologies. To ensure that partners engage with each other extensively, all three partners are contributing to the implementation of almost all work packages
Summary of project results
The project aimed to develop an advanced methodology for the assessment of coronary lesions in non-culprit arteries of ACS (Acute Coronary Syndrome) patients. This involved integrating heterogeneous data types into a lesion-specific risk score to determine which non-culprit lesions require treatment. A secondary goal was to assess microvascular disease by developing a methodology that determines the contribution of epicardial stenoses and microvascular dysfunction to the patient''s overall pathologic state.
The project mobilized various research efforts to develop a cloud-based solution integrating tools for assessing non-culprit lesions in routine clinical workflows. It involved developing a risk score specific for non-culprit lesions in ACS patients, which assesses their vulnerability to cause a Major Adverse Cardiac Event (MACE). Additionally, several clinical studies were conducted, including a retrospective observational study and a prospective study, to evaluate the developed methodologies and tools.
As a result of the project, an innovative lesion-specific risk stratification model was developed, leveraging computer vision, computational modeling, and machine learning. This model is expected to significantly enhance clinical decision-making for ACS patients by identifying high-risk coronary lesions that require intervention. The project also contributed to the integration of advanced diagnostic tools into routine clinical practice, benefiting healthcare providers and patients by improving the accuracy and efficiency of coronary lesion assessments.
Summary of bilateral results
The project contributes to reducing social and economic disparities by improving the diagnosis and treatment of cardiovascular diseases, which are a leading cause of mortality globally. By developing advanced diagnostic tools and integrating them into routine clinical workflows, the project enhances the quality of healthcare services available to patients, potentially reducing the incidence of adverse cardiac events and improving patient outcomes.The project also strengthens bilateral cooperation by involving international collaboration between institutions, such as NTNU and other partners, fostering the exchange of knowledge and expertise in cardiovascular research and healthcare innovation. This collaboration enhanced the capacity of participating institutions to address complex health challenges and contributed to the overall goals of the EEA Grants program.