Periodic Reporting for period 1 - Sim4DFlow (Personalized intracranial aneurysm rupture prognosis using Simulation-Based 4D Flow MRI and Machine Learning)
Período documentado: 2021-09-01 hasta 2023-08-31
The Sim4DFlow project was supported by the European Union under Horizon 2020 to investigate and address an unmet clinical challenge: the prediction of unruptured intracranial aneurysm (UIA) failure. UIA is a prevalent cerebrovascular disorder with a substantial number of patients in the EU at high risk of life-threatening aneurysm rupture and hemorrhage. The core objective of the Sim4DFlow project was to pioneer a novel simulation-based imaging framework that integrated advanced computational techniques, including 4D flow magnetic resonance imaging (MRI), machine learning (ML), and computational fluid dynamics (CFD). This innovative approach aimed to identify factors contributing to UIA rupture and apply 4D flow MRI, ML algorithms, and CFD models to achieve advanced quantification of personalized rupture risk assessments in UIA patients.
Overall Objectives:
1. Enhancing Rupture Risk Assessment:
- The primary objective of the project was to reliably quantify rupture risk specifically tailored to each UIA patient. This involved identifying factors contributing to UIA rupture and utilizing advanced computational techniques, such as 4D flow Magnetic Resonance Imaging (MRI), Machine Learning (ML) algorithms, and Computational Fluid Dynamics (CFD) models.
2. Optimizing Management Strategies:
- Given the complexity of UIA management decisions, the project contributed towards optimizing surgical intervention strategies, including determining the timing and type of intervention appropriate (e.g. clipping, coils, stent, flow diverter) for the treatment of intracranial aneurysms.
3. Technological Advancement:
- The core technological aim of the project was to develop a novel simulation-based imaging framework integrating 4D flow MRI, ML, and CFD. This framework held the potential to significantly advance the technological landscape for UIA assessment and rupture risk prediction.
4. Laboratory Testing and Validation:
- Subsequently, Sim4DFlow rigorously tested and validated the developed framework in a laboratory setting against relevant data from UIA patients. This testing phase ensured the framework's prognostic capacity and reliability in real-world scenarios.
By aligning technological innovation with clinical needs, the Sim4DFlow project not only sought to enhance the understanding of UIA rupture risk but also strove to revolutionize the personalized management of this critical cerebrovascular condition.
Overall Objectives:
1. Enhancing Rupture Risk Assessment:
- The primary objective of the project was to reliably quantify rupture risk specifically tailored to each UIA patient. This involved identifying factors contributing to UIA rupture and utilizing advanced computational techniques, such as 4D flow Magnetic Resonance Imaging (MRI), Machine Learning (ML) algorithms, and Computational Fluid Dynamics (CFD) models.
2. Optimizing Management Strategies:
- Given the complexity of UIA management decisions, the project contributed towards optimizing surgical intervention strategies, including determining the timing and type of intervention appropriate (e.g. clipping, coils, stent, flow diverter) for the treatment of intracranial aneurysms.
3. Technological Advancement:
- The core technological aim of the project was to develop a novel simulation-based imaging framework integrating 4D flow MRI, ML, and CFD. This framework held the potential to significantly advance the technological landscape for UIA assessment and rupture risk prediction.
4. Laboratory Testing and Validation:
- Subsequently, Sim4DFlow rigorously tested and validated the developed framework in a laboratory setting against relevant data from UIA patients. This testing phase ensured the framework's prognostic capacity and reliability in real-world scenarios.
By aligning technological innovation with clinical needs, the Sim4DFlow project not only sought to enhance the understanding of UIA rupture risk but also strove to revolutionize the personalized management of this critical cerebrovascular condition.
The Sim4DFlow project achieved significant milestones throughout its duration, involving the collection of a comprehensive dataset comprising 694 image sets sourced from diverse outlets, including open-access databases and specific institutions. These sets, characterized by demographic and geometric features, played a crucial role in training a Machine Learning algorithm. Additionally, the project conducted 273 Computational Fluid Dynamics (CFD) simulations, yielding valuable insights into hemodynamic parameters.
The project's outcomes were disseminated through presentations at two international conferences, with abstracts published as follows:
[1] N. Aristokleous, K.G. Achilleos, M Hadjicharalambous, A.S. Anayiotos, C.S. Pattichis, V. Vavourakis, "Intracranial aneurysm predictions with the use of morphometric features in a Machine Learning approach," 27th Congress of the European Society of Biomechanics, June 26-29, 2022, Porto, Portugal.
[2] N. Aristokleous, K.G. Achilleos, C.S. Pattichis, V. Vavourakis, "Initial strides for intracranial aneurysm predictions with the use of morphometric features in a Machine Learning approach," 9th World Congress of Biomechanics, Taipei, Taiwan, 10-14 July 2022.
[3] N. Aristokleous, N. Prentza, D. Flouri, A. Kakas, C.S. Pattichi, V. Vavourakis, “Machine Learning Approach for Intracranial Aneurysm Prediction using Morphometric Features”, 29th Congress of the European Society of Biomechanics, June 30- July 3, 2024, Edinburgh, UK.
Furthermore, a peer-reviewed paper titled "Intracranial Aneurysm Rupture Risk Prediction Based on Morphometric Features," authored by N. Aristokleous, N. Prentza, C.S. Pattichis, and V. Vavourakis, has been prepared for submission for review in the Annals of Biomedical Engineering journal.
In parallel, the project team is currently in progress on another paper. This forthcoming paper aims to extend the scope by incorporating both morphometric and hemodynamic features for a more comprehensive analysis. A tentative title for this upcoming paper is "Integrated Morphometric and Hemodynamic Features for Enhanced Intracranial Aneurysm Rupture Risk Prediction." This work reflects the ongoing commitment to advancing the understanding of intracranial aneurysm dynamics and further contributing to the science in the field.
The project's outcomes were disseminated through presentations at two international conferences, with abstracts published as follows:
[1] N. Aristokleous, K.G. Achilleos, M Hadjicharalambous, A.S. Anayiotos, C.S. Pattichis, V. Vavourakis, "Intracranial aneurysm predictions with the use of morphometric features in a Machine Learning approach," 27th Congress of the European Society of Biomechanics, June 26-29, 2022, Porto, Portugal.
[2] N. Aristokleous, K.G. Achilleos, C.S. Pattichis, V. Vavourakis, "Initial strides for intracranial aneurysm predictions with the use of morphometric features in a Machine Learning approach," 9th World Congress of Biomechanics, Taipei, Taiwan, 10-14 July 2022.
[3] N. Aristokleous, N. Prentza, D. Flouri, A. Kakas, C.S. Pattichi, V. Vavourakis, “Machine Learning Approach for Intracranial Aneurysm Prediction using Morphometric Features”, 29th Congress of the European Society of Biomechanics, June 30- July 3, 2024, Edinburgh, UK.
Furthermore, a peer-reviewed paper titled "Intracranial Aneurysm Rupture Risk Prediction Based on Morphometric Features," authored by N. Aristokleous, N. Prentza, C.S. Pattichis, and V. Vavourakis, has been prepared for submission for review in the Annals of Biomedical Engineering journal.
In parallel, the project team is currently in progress on another paper. This forthcoming paper aims to extend the scope by incorporating both morphometric and hemodynamic features for a more comprehensive analysis. A tentative title for this upcoming paper is "Integrated Morphometric and Hemodynamic Features for Enhanced Intracranial Aneurysm Rupture Risk Prediction." This work reflects the ongoing commitment to advancing the understanding of intracranial aneurysm dynamics and further contributing to the science in the field.
The Sim4DFlow project transcended the current state of the art by seamlessly integrating 4D-Flow MRI, Computational Fluid Dynamics (CFD) simulations, and Machine Learning to predict intracranial aneurysm rupture risk. This innovative synthesis surpasses traditional methodologies, providing a holistic understanding of both hemodynamic and morphometric factors influencing aneurysm behavior.
The anticipated results until the project's conclusion encompass the development of refined predictive models. These models hold the potential to significantly enhance clinical decision-making and treatment strategies for patients with intracranial aneurysms. The interdisciplinary approach employed in Sim4DFlow not only contributes to the advancement of scientific knowledge but also holds transformative implications for medical practice.
The socio-economic impact of the project is substantial. Improved diagnostic tools may lead to more efficient healthcare resource allocation and personalized patient care, thereby potentially reducing treatment costs. The project's overarching objective is to contribute to the optimization of healthcare practices, aligning with broader societal needs for effective, cost-conscious, and patient-centered medical solutions.
After the completion of the Sim4DFlow project, the focus will shift towards translating its findings and innovations into practical applications. This involves collaborating with relevant stakeholders, such as medical institutions, healthcare providers, and policymakers, to explore the integration of the project's predictive models and methodologies into routine clinical practice. Moreover, there is a strategic vision to explore the possibility of creating a startup company dedicated to implementing and further developing the technologies and insights derived from Sim4DFlow. This startup endeavor aims to bring the project's advancements to a broader audience, facilitating widespread access to enhanced intracranial aneurysm risk assessments and optimized treatment strategies. Additionally, efforts will be made to disseminate knowledge through publications, presentations, and educational initiatives, fostering a broader understanding of the project's impact within the medical and research communities. The project's influence on healthcare practices and resource allocation will be further explored, aiming to contribute to more efficient and patient-centered medical solutions at a societal level.
The anticipated results until the project's conclusion encompass the development of refined predictive models. These models hold the potential to significantly enhance clinical decision-making and treatment strategies for patients with intracranial aneurysms. The interdisciplinary approach employed in Sim4DFlow not only contributes to the advancement of scientific knowledge but also holds transformative implications for medical practice.
The socio-economic impact of the project is substantial. Improved diagnostic tools may lead to more efficient healthcare resource allocation and personalized patient care, thereby potentially reducing treatment costs. The project's overarching objective is to contribute to the optimization of healthcare practices, aligning with broader societal needs for effective, cost-conscious, and patient-centered medical solutions.
After the completion of the Sim4DFlow project, the focus will shift towards translating its findings and innovations into practical applications. This involves collaborating with relevant stakeholders, such as medical institutions, healthcare providers, and policymakers, to explore the integration of the project's predictive models and methodologies into routine clinical practice. Moreover, there is a strategic vision to explore the possibility of creating a startup company dedicated to implementing and further developing the technologies and insights derived from Sim4DFlow. This startup endeavor aims to bring the project's advancements to a broader audience, facilitating widespread access to enhanced intracranial aneurysm risk assessments and optimized treatment strategies. Additionally, efforts will be made to disseminate knowledge through publications, presentations, and educational initiatives, fostering a broader understanding of the project's impact within the medical and research communities. The project's influence on healthcare practices and resource allocation will be further explored, aiming to contribute to more efficient and patient-centered medical solutions at a societal level.