Deep learning to estimate aging from chest imaging

年龄是许多慢性疾病的重要危险因素。然而，由于我们都以不同的速度衰老，实足年龄是衰老的一个不完美的衡量标准，更准确地估计一个人的真实生理年龄是可取的。这项拟议的研究将开发和测试基于人工智能的胸部成像数据衰老指标，并将探索这些指标是否可以改善基于年龄的心血管疾病和癌症预防临床指南。近几十年来，预期寿命不断增加，然而，在过去的50年里，没有显著发病率的寿命年保持稳定，给社会和医疗保健系统带来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途径。特别是，预防和早期发现具有高度社会经济重要性的疾病，如癌症和心血管疾病，是增进健康和长寿的优先事项。生物年龄是一个概念，用来估计未被实际年龄捕获的衰老率差异。近年来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。但在临床常规中相关性有限。医学成像可能是一种有效的方法，通过测量年龄相关的变化，基于图像中可见的解剖改变，如脊柱的退行性变化，心脏和血管的扩张以及肺实质的变化。与分子测量不同，基于图像的衰老反射可以利用常规临床护理期间获得的数据来计算机会。此外，这些措施可以估计特定器官系统的衰老，这可能比一般的生物年龄估计改善疾病特异性预防措施。本研究提出的使用深度学习的基于图像的衰老估计将解决这一未满足的需求，并可能有助于个性化慢性疾病筛查和预防的临床决策。我的初步数据表明，深度学习可以从胸片图像中估计胸片年龄，并且在大型多中心临床试验数据中，胸片年龄比年表年龄更能预测寿命。拟议的项目将建立在这些有希望的初步发现的基础上，有可能扩大应用到其他成像模式，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.