Accurately evaluating the disease risks after low-dose ionizing radiation (IR) exposure are crucial for protecting public health, setting safety standards, and advancing research in radiation safety. However, while much is known about the disease risks of high-dose irradiation, risk estimates at low dose remains controversial. To date, five different parametric models (supra-linear, linear no threshold, threshold, quadratic, and hormesis) for low doses have been studied in the literature. Different dose-response models may lead to inconsistent or even conflicting results. In this manuscript, we introduce a data-driven deep neural network (DNN) model designed to evaluate dose-response models at low doses using Life Span Study (LSS) data. DNNs possess the capability to approximate any continuous function with an adequate number of nodes in the hidden layers. Being data-driven, they circumvent the challenges associated with misspecification inherent in parametric models. Our simulation study highlights the effectiveness of DNNs as a valuable tool for precisely identifying dose-response models from available data. New findings from the LSS study provide robust support for a linear quadratic (LQ) dose-response model at low doses. While the linear no threshold (LNT) model tends to overestimate disease risk at very low doses and underestimate health risk at relatively high doses, it remains a reasonable approximation for the LQ model, given the minor impact of the quadratic term at low doses. Our demonstration underscores the power of DNNs in facilitating comprehensive investigations into dose-response associations.

中文翻译：

---

低剂量电离辐射后的剂量反应：数据驱动的深度神经网络模型的寿命研究证据

准确评估低剂量电离辐射（IR）暴露后的疾病风险对于保护公众健康、制定安全标准和推进辐射安全研究至关重要。然而，尽管人们对高剂量辐射的疾病风险了解很多，但低剂量辐射的风险估计仍然存在争议。迄今为止，文献中已经研究了低剂量的五种不同参数模型（超线性、线性无阈值、阈值、二次和毒物兴奋效应）。不同的剂量反应模型可能会导致不一致甚至相互矛盾的结果。在这篇手稿中，我们介绍了一种数据驱动的深度神经网络（DNN）模型，旨在使用寿命研究（LSS）数据评估低剂量的剂量反应模型。 DNN 能够通过隐藏层中足够数量的节点来逼近任何连续函数。由于数据驱动，它们规避了参数模型固有的错误指定带来的挑战。我们的模拟研究强调了 DNN 作为从可用数据中精确识别剂量反应模型的宝贵工具的有效性。 LSS 研究的新发现为低剂量下的线性二次 (LQ) 剂量反应模型提供了强有力的支持。虽然线性无阈值 (LNT) 模型往往会高估极低剂量下的疾病风险，并低估相对较高剂量下的健康风险，但考虑到低剂量下二次项的影响较小，它仍然是 LQ 模型的合理近似值。我们的演示强调了 DNN 在促进剂量反应关联的全面研究方面的力量。