Cardiovascular disease (CVD) represents a major public health issue, claiming numerous lives. This study aimed to demonstrate the advantages of employing artificial intelligence (AI) models to improve the prediction of CVD risk using a large cohort of relatively healthy adults aged 70 years or more. In this study, deep learning (DL) models provide enhanced predictions (DeepSurv: C-index = 0.662, Integrated Brier Score (IBS) = 0.046; Neural Multi-Task Logistic Regression (NMTLR): C-index = 0.660, IBS = 0.047), as compared to the conventional (Cox: C-index = 0.634, IBS = 0.048) and machine learning (Random Survival Forest (RSF): C-index = 0.641, IBS = 0.048) models. The risk scores generated by the DL models also demonstrated superior performance. Moreover, AI models (NMTLR, DeepSurv, and RSF) were more effective, requiring the treatment of only 9 to 10 patients to prevent one CVD event, compared to the conventional model requiring treatment of nearly four times higher number of patients (NNT = 38). In summary, AI models, particularly DL models, possess superior predictive capabilities that can enhance patient treatment in a more cost-effective manner. Nonetheless, AI tools should serve to complement and assist healthcare professionals, rather than supplant them. The DeepSurv model, selected due to its relatively superior performance, is deployed in the form of web application locally, and is accessible on GitHub (https://github.com/Robidar/Chuchu_Depl). Finally, as we have demonstrated the benefit of using AI for reassessment of an existing CVD risk score, we recommend other infamous risk scores undergo similar reassessment.

心血管疾病 （CVD） 是一个重大的公共卫生问题，夺走了无数人的生命。本研究旨在证明使用大量 70 岁或以上相对健康的成年人使用人工智能 （AI） 模型来改进 CVD 风险预测的优势。在这项研究中，深度学习 （DL） 模型提供了增强的预测 （DeepSurv：C 指数 = 0.662，综合 Brier 评分 （IBS） = 0.046;神经多任务 Logistic 回归 （NMTLR）：C 指数 = 0.660，IBS = 0.047），与传统 （Cox： C-index = 0.634，IBS = 0.048） 和机器学习 （随机生存森林 （RSF）：C-指数 = 0.641，IBS = 0.048） 模型相比。DL 模型生成的风险评分也显示出卓越的性能。此外，AI 模型（NMTLR、DeepSurv 和 RSF）更有效，与传统模型需要治疗近四倍的患者数量 （NNT = 38） 相比，只需要治疗 9 到 10 名患者即可预防一次 CVD 事件。总之，AI 模型，尤其是 DL 模型，具有卓越的预测能力，可以以更具成本效益的方式增强患者治疗。尽管如此，人工智能工具应该用于补充和协助医疗保健专业人员，而不是取代他们。DeepSurv 模型因其相对优越的性能而被选中，以 Web 应用程序的形式在本地部署，并且可以在 GitHub （https://github.com/Robidar/Chuchu_Depl） 上访问。最后，正如我们已经证明了使用 AI 重新评估现有 CVD 风险评分的好处，我们建议对其他臭名昭著的风险评分进行类似的重新评估。