Deep learning to estimate aging from chest imaging

慢性年龄是许多慢性病的重要危险因素。然而，由于我们都以不同的速度变老，时间年龄并不是衡量衰老的完美指标，更准确地估计一个人的真实生物年龄是可取的。这项拟议的研究将根据胸部成像数据开发和测试基于人工智能的衰老指标，并将探索这些措施是否可以改进基于年龄的临床指南，以预防心血管疾病和癌症。近几十年来，预期寿命不断增加，然而，在过去50年中，未发生重大疾病的寿命保持稳定，给社会和医疗保健系统造成了重大的社会经济负担。因此，延缓发病是改善健康老龄化的一种有希望的方法。特别是，预防和及早发现癌症和心血管疾病等具有高度社会经济重要性的疾病是增加健康和寿命的优先事项。生物年龄是一个概念，用来估计年龄不能反映的老龄化速度的差异。近年来，已经发展了几种生物年龄的测量方法，包括血液、功能和生理方法。然而，它们在临床常规中的相关性有限。医学成像可能是一种有效的估计衰老的方法，它基于图像中可见的解剖变化来测量与年龄相关的变化，如脊柱退行性变化、心脏和血管扩张以及肺实质的变化。与分子测量不同，基于图像的衰老反映可以利用常规临床护理过程中获得的数据进行机会性计算。此外，这些措施可以估计特定器官系统的老化，这可能会改进针对疾病的预防措施，而不是一般的生物年龄估计。这项研究中提出的基于图像的深度学习对衰老的估计将解决这一未得到满足的需求，并可能有助于为慢性病筛查和预防制定个性化的临床决策。我的初步数据显示，深度学习可以从胸部X光图像估计胸部X光年龄，在大型多中心临床试验数据中，这个胸部X光年龄预测寿命比实际年龄更好。拟议的项目将建立在这些有希望的初步发现的基础上，有可能扩大到其他成像模式的应用，并利用多组学数据来推断衰老的原因。

Chronologic age is an important risk factor for many chronic diseases. However, as we all age at different rates chronological age is an imperfect measure of aging and more accurate estimates of one’s true biological age are desirable. The proposed study will develop and test artificial intelligence-based measures of aging from chest imaging data and will explore whether these measures can improve chronologic age-based clinical guidelines for prevention of cardiovascular disease and cancer. Over recent decades life expectancy has continuously increased, however, the years of life without significant morbidity have remained stable over the last 5 decades resulting in significant socioeconomic burden to society and the healthcare system. Therefore, delaying the onset of morbidity is a promising way to improve healthy aging. In particular, prevention and early detection of diseases of high socioeconomic importance such as cancer and cardiovascular disease are a priority to increase health and longevity. Biological age is a concept to estimate the differences in rates of aging not captured by chronologic age. Several measures of biological age have been developed in recent years including blood, functional and physiological approaches. However, there relevance in clinical routine is limited. Medical imaging may be an effective way to estimate aging by measuring age-related changes based on anatomical alterations visible in the image such as degenerative changes of the spine, dilation of the heart and vasculature and changes in lung parenchyma. Unlike molecular measures, image-based reflections of aging can be calculated opportunistically using data acquired during routine clinical care. Furthermore, these measures can estimate aging of specific organ systems, which may improve disease-specific preventive measures over general estimates of biological age. The image-based estimates of aging using deep learning proposed in this study will address this unmet need and may help to personalize clinical decision-making for chronic disease screening and prevention. My preliminary data show that deep learning can estimate a chest x-ray age from a chest radiograph image and that this chest x-ray age predicts longevity better than chronologic age in large multicenter clinical trial data. The proposed project will build on these promising initial findings with the potential to broaden the application to other imaging modalities and to leverage multi-omics data to infer causes of aging.