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Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: Data modeling validation uncertainty and predictions

机译：基于图像的建模可更好地理解和评估动脉粥样硬化斑块的进展和脆弱性：数据建模验证不确定性和预测

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摘要

Medical imaging and image-based modeling have made considerable progress in recent years in identifying atherosclerotic plaque morphological and mechanical risk factors which may be used in developing improved patient screening strategies. However, a clear understanding is needed about what we have achieved and what is really needed to translate research to actual clinical practices and bring benefits to public health. Lack of in vivo data and clinical events to serve as gold standard to validate model predictions is a severe limitation. While this perspective paper provides a review of the key steps and findings of our group in image-based models for human carotid and coronary plaques and a limited review of related work by other groups, we also focus on grand challenges and uncertainties facing the researchers in the field to develop more accurate and predictive patient screening tools.
机译：近年来，医学成像和基于图像的建模在识别动脉粥样硬化斑块的形态学和机械危险因素方面取得了长足的进步，这些因素可用于开发改进的患者筛查策略。但是，需要对我们已经取得的成就以及将研究成果转化为实际的临床实践并为公众健康带来好处的真​​正需要有一个清晰的了解。缺乏体内数据和临床事件作为验证模型预测的金标准是一个严重的局限。尽管本观点论文回顾了我们小组在基于图像的人颈动脉和冠状动脉斑块模型中的关键步骤和发现，并对其他小组的相关工作进行了有限的回顾，但我们也关注于研究人员面临的巨大挑战和不确定性该领域将开发更准确和可预测的患者筛查工具。

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