Lotte Rijken, Sabrina Zwetsloot, Stefan Smorenburg, Jelmer Wolterink, Ivana Išgum, Henk Marquering, Jan van Duivenvoorde, Corrette Ploem, Roosmarie Jessen, Fabio Catarinella, Regent Lee, Katarzyna Bera, Jenny Buisan, Ping Zhang, Marina Dias-Neto, Juliette Raffort, Fabien Lareyre, Catelijne Muller, Igor Koncar, Ivan Tomic, Maja Živković, Tamara Djuric, Aleksandra Stankovic, Maarit Venermo, Riikka Tulamo, Christian-Alexander Behrendt, Noeska Smit, Marlies Schijven, Bert-Jan van den Born, Ronak Delewi, Vincent Jongkind, Venkat Ayyalasomayajula, Kak Khee Yeung
{"title":"Developing Trustworthy Artificial Intelligence Models to Predict Vascular Disease Progression: the VASCUL-AID-RETRO Study Protocol.","authors":"Lotte Rijken, Sabrina Zwetsloot, Stefan Smorenburg, Jelmer Wolterink, Ivana Išgum, Henk Marquering, Jan van Duivenvoorde, Corrette Ploem, Roosmarie Jessen, Fabio Catarinella, Regent Lee, Katarzyna Bera, Jenny Buisan, Ping Zhang, Marina Dias-Neto, Juliette Raffort, Fabien Lareyre, Catelijne Muller, Igor Koncar, Ivan Tomic, Maja Živković, Tamara Djuric, Aleksandra Stankovic, Maarit Venermo, Riikka Tulamo, Christian-Alexander Behrendt, Noeska Smit, Marlies Schijven, Bert-Jan van den Born, Ronak Delewi, Vincent Jongkind, Venkat Ayyalasomayajula, Kak Khee Yeung","doi":"10.1177/15266028251313963","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Abdominal aortic aneurysms (AAAs) and peripheral artery disease (PAD) are two vascular diseases with a significant risk of major adverse cardiovascular events and mortality. A challenge in current disease management is the unpredictable disease progression in individual patients. The VASCUL-AID-RETRO study aims to develop trustworthy multimodal predictive artificial intelligence (AI) models for multiple tasks including risk stratification of disease progression and cardiovascular events in patients with AAA and PAD.</p><p><strong>Methods: </strong>The VASCUL-AID-RETRO study will collect data from 5000 AAA and 6000 PAD patients across multiple European centers of the VASCUL-AID consortium using electronic health records from 2015 to 2024. This retrospectively-collected data will be enriched with additional data from existing biobanks and registries. Multimodal data, including clinical records, radiological imaging, proteomics, and genomics, will be collected to develop AI models predicting disease progression and cardiovascular risks. This will be done while integrating the international ethics guidelines and legal standards for trustworthy AI, to ensure a socially-responsible data integration and analysis.</p><p><strong>Proposed analyses: </strong>A consensus-based variable list of clinical parameters and core outcome set for both diseases will be developed through meetings with key opinion leaders. Blood, plasma, and tissue samples from existing biobanks will be analyzed for proteomic and genomic variations. AI models will be trained on segmented AAA and PAD artery geometries for estimation of hemodynamic parameters to quantify disease progression. Initially, risk prediction models will be developed for each modality separately, and subsequently, all data will be combined to be used as input to multimodal prediction models. During all processes, data security, data quality, and ethical guidelines and legal standards will be carefully considered. As a next step, the developed models will be further adjusted with prospective data and internally validated in a prospective cohort (VASCUL-AID-PRO study).</p><p><strong>Conclusion: </strong>The VASCUL-AID-RETRO study will utilize advanced AI techniques and integrate clinical, imaging, and multi-omics data to predict AAA and PAD progression and cardiovascular events.</p><p><strong>Clinical trial registration: </strong>The VASCUL-AID-RETRO study is registered at www.clinicaltrials.gov under the identification number NCT06206369.</p><p><strong>Clinical impact: </strong>The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine. By integrating comprehensive data sets including clinical, imaging, and multi-omics data, these models have the potential to provide accurate risk stratification for disease progression and cardiovascular events. An innovation lies in the extensive European data set in combination with multimodal analyses approaches, which enables the development of advanced models to facilitate better understanding of disease mechanisms and progression. For clinicians, this means that more precise, individualized treatment plans can be established, ultimately aiming to improve patient outcomes.</p>","PeriodicalId":50210,"journal":{"name":"Journal of Endovascular Therapy","volume":" ","pages":"15266028251313963"},"PeriodicalIF":1.7000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Endovascular Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15266028251313963","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Abdominal aortic aneurysms (AAAs) and peripheral artery disease (PAD) are two vascular diseases with a significant risk of major adverse cardiovascular events and mortality. A challenge in current disease management is the unpredictable disease progression in individual patients. The VASCUL-AID-RETRO study aims to develop trustworthy multimodal predictive artificial intelligence (AI) models for multiple tasks including risk stratification of disease progression and cardiovascular events in patients with AAA and PAD.
Methods: The VASCUL-AID-RETRO study will collect data from 5000 AAA and 6000 PAD patients across multiple European centers of the VASCUL-AID consortium using electronic health records from 2015 to 2024. This retrospectively-collected data will be enriched with additional data from existing biobanks and registries. Multimodal data, including clinical records, radiological imaging, proteomics, and genomics, will be collected to develop AI models predicting disease progression and cardiovascular risks. This will be done while integrating the international ethics guidelines and legal standards for trustworthy AI, to ensure a socially-responsible data integration and analysis.
Proposed analyses: A consensus-based variable list of clinical parameters and core outcome set for both diseases will be developed through meetings with key opinion leaders. Blood, plasma, and tissue samples from existing biobanks will be analyzed for proteomic and genomic variations. AI models will be trained on segmented AAA and PAD artery geometries for estimation of hemodynamic parameters to quantify disease progression. Initially, risk prediction models will be developed for each modality separately, and subsequently, all data will be combined to be used as input to multimodal prediction models. During all processes, data security, data quality, and ethical guidelines and legal standards will be carefully considered. As a next step, the developed models will be further adjusted with prospective data and internally validated in a prospective cohort (VASCUL-AID-PRO study).
Conclusion: The VASCUL-AID-RETRO study will utilize advanced AI techniques and integrate clinical, imaging, and multi-omics data to predict AAA and PAD progression and cardiovascular events.
Clinical trial registration: The VASCUL-AID-RETRO study is registered at www.clinicaltrials.gov under the identification number NCT06206369.
Clinical impact: The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine. By integrating comprehensive data sets including clinical, imaging, and multi-omics data, these models have the potential to provide accurate risk stratification for disease progression and cardiovascular events. An innovation lies in the extensive European data set in combination with multimodal analyses approaches, which enables the development of advanced models to facilitate better understanding of disease mechanisms and progression. For clinicians, this means that more precise, individualized treatment plans can be established, ultimately aiming to improve patient outcomes.
期刊介绍:
The Journal of Endovascular Therapy (formerly the Journal of Endovascular Surgery) was established in 1994 as a forum for all physicians, scientists, and allied healthcare professionals who are engaged or interested in peripheral endovascular techniques and technology. An official publication of the International Society of Endovascular Specialists (ISEVS), the Journal of Endovascular Therapy publishes peer-reviewed articles of interest to clinicians and researchers in the field of peripheral endovascular interventions.