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.
腹主动脉瘤(AAAs)和外周动脉疾病(PAD)是两种具有重大心血管不良事件和死亡风险的血管疾病。当前疾病管理的一个挑战是个体患者不可预测的疾病进展。vascull - aid - retro研究旨在开发可信赖的多模式预测人工智能(AI)模型,用于AAA和PAD患者疾病进展和心血管事件的风险分层等多项任务。方法:vascull - aid - retro研究将收集来自vascull - aid联盟多个欧洲中心的5000名AAA和6000名PAD患者的数据,使用2015年至2024年的电子健康记录。这些回顾性收集的数据将与来自现有生物库和登记处的额外数据相补充。将收集包括临床记录、放射成像、蛋白质组学和基因组学在内的多模式数据,以开发预测疾病进展和心血管风险的人工智能模型。这将在整合可信赖人工智能的国际伦理准则和法律标准的同时进行,以确保对社会负责任的数据整合和分析。提议的分析:将通过与主要意见领袖的会议,制定一份基于共识的临床参数和两种疾病的核心结果的可变清单。将对现有生物库中的血液、血浆和组织样本进行蛋白质组学和基因组变异分析。人工智能模型将在分段的AAA和PAD动脉几何形状上进行训练,以估计血流动力学参数,量化疾病进展。首先,将针对每种模式分别开发风险预测模型,随后,将所有数据组合起来,作为多模式预测模型的输入。在所有过程中,将仔细考虑数据安全、数据质量、道德准则和法律标准。下一步,将使用前瞻性数据进一步调整已开发的模型,并在前瞻性队列(VASCUL-AID-PRO研究)中进行内部验证。结论:vascular - aid - retro研究将利用先进的人工智能技术,整合临床、影像学和多组学数据,预测AAA和PAD的进展和心血管事件。临床试验注册:vascull - aid - retro研究注册于www.clinicaltrials.gov,识别号为NCT06206369。临床影响:vascull - aid - retro研究旨在通过开发腹主动脉瘤或外周动脉疾病患者人工智能驱动的多模态预测模型,提高血管外科的临床实践水平,增强个性化医疗。通过整合包括临床、影像学和多组学数据在内的综合数据集,这些模型有可能为疾病进展和心血管事件提供准确的风险分层。一项创新在于广泛的欧洲数据集与多模式分析方法相结合,从而能够开发先进的模型,以促进对疾病机制和进展的更好理解。对于临床医生来说,这意味着可以制定更精确、更个性化的治疗计划,最终旨在改善患者的治疗效果。
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.