PDF(Pubmed)
Abstract：
Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.
摘要：
人工智能（AI）算法具有彻底改变放射学的潜力。然而,发表的文献中有很大一部分缺乏透明度和可重复性，这阻碍了临床翻译的持续进展。尽管已经提出了一些报告准则，确定解决这些问题的实际手段仍然具有挑战性。这里,我们展示了基于云的基础设施在实施和共享透明和可重复的基于AI的放射学管道方面的潜力。我们展示了通过检索云托管数据实现的端到端可重复性，通过数据预处理，深度学习推理，和后处理，分析和报告最终结果。我们成功地实现了两个不同的用例，从最近关于基于AI的癌症成像生物标志物的文献开始。使用云托管的数据和计算，我们确认了这些研究的结果,并将验证扩展到其中一个用例以前未见过的数据.此外，我们为社区提供透明且易于扩展的管道示例，这些示例对更广泛的肿瘤学领域产生影响。我们的方法展示了云资源在实施、分享,并使用可复制和透明的人工智能管道，这可以加速转化为临床解决方案。
