NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3)

NSF融合加速器支持以使用为灵感、以团队为基础的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生对社会有价值的成果。该项目名为融合加速器轨道D：可扩展、可跟踪的人工智能成像转换：从创新到加速影响的实施(海峡i3)，解决了科学和工程之间的根本差距，这些差距阻碍了使用医学成像数据的人工智能模型的有效使用。该项目将利用可用于医学成像的大量开放数据集工作，包括新冠肺炎的成像资源。每年也有数以千计的人工智能模型发表在科学文献中，以获取此类数据。然而，这些资源在规模上并不是始终如一的可用，也不能被验证用于临床应用。该项目包括解决这一问题的三个主要领域。主要领域1通过可跟踪的数据注释使对数据集的访问变得大众化。推力领域2改变了数据的评估和同行审查程序，以确保对技术进行公平和一致的评估。推力领域3的目标是模型的可重复执行和比较，以促进将其转化为实践。在第一阶段，这一融合加速器项目将通过加强对新冠肺炎肺炎的放射评估，创造直接的公共卫生和技术利益。在第二阶段，它将把这些好处扩展到一个跨越多个医学成像领域的医学成像生态系统。通过由专业人士领导的工作室、商定的A/B测试研究和有组织的外联，让各种确定的社区参与进来，将产生更广泛的影响。所有项目都尽可能使用开放软件和商用硬件，因此这个项目在可伸缩图像数据验证方面的创新将促进开源软件、开放科学、可复制科学和可发现科学的其他相关工作。该项目致力于实现对如何在医学成像、算法设计和医学科学中验证和转换以模型为中心的人工智能的根本反思。智力活动围绕三个研究推动力组织，每个研究推动力都解决了目前基于人工智能的医学成像工具的开发和翻译面临的一个基本挑战。其中一个研究重点是创建一个与临床成像和研究研究兼容的轻量级数据来源和注释界面。第二个是促进人工智能架构的快速创新，同时创建增强的验证/同行审查过程，以避免不可复制的实施和模型的过度培训。第三个重点是将这些努力整合到一个新的模型动物园中，以提供强大的验证、评估和翻译能力。这项研究工作将利用合作团队的核心科学创新，该团队由来自一所大学(Vanderbilt)、一个医疗中心(Vanderbilt Medical Center)、两个行业合作伙伴(MD.ai、Kaggle)和一个专业协会(SIM)的成员组成，以及广泛使用的开源平台。在融合加速器的第一阶段，该项目将专注于为新冠肺炎新创建的公共和私人数据集。第二阶段将把这种方法扩展到不同的医学成像模式，包括皮肤科和眼科。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project, Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for accelerated Impact (STRAIT I3), addresses fundamental gaps between the science and the engineering that is preventing the effective use of AI models with medical imaging data. The project will leverage the large number of open dataset efforts available for medical imaging, including imaging resources for COVID-19. Thousands of AI models are also published in the scientific literature each year for such data. Yet, these resources are not consistently accessible at scale nor are they able to be validated for clinical application. This project includes three thrust areas to address this problem. Thrust Area 1 democratizes access to data sets through traceable data annotation. Thrust Area 2 transforms the assessment and peer review process for data, to ensure fair and consistent evaluation of technologies. Thrust Area 3 targets reproducible execution and comparison of models to facilitate translation to practice. In Phase I, this Convergence Accelerator project will create direct public health and technology benefits by enhancing the radiological assessment of COVID-19 pneumonia. In Phase II, it will extend these benefits into a medical imaging ecosystem spanning multiple medical imaging domains. Broader impacts will be achieved by engaging various identified communities through professionally led studios, consented A/B testing studies, and structured outreach. All project thrusts utilized open software and commodity hardware, wherever possible, so that the innovations from this project on scalable image data validation will enhance other related efforts in open source software, open science, reproducible science, and findable science.This project works towards achieving a fundamental rethinking in how model-centric AI could be validated and translated in medical imaging, algorithm design, and medical science. The intellectual activities are organized around three research thrusts, each addressing an essential challenge that currently confronts the development and translation of AI-based medical imaging tools. One research thrust is on creating a lightweight data provenance and annotation interface compatible with both clinical imaging and research studies. The second is on facilitating rapid innovation in AI architectures while creating an enhanced validation/peer review process to avoid irreproducible implementations and overtraining of models. The third thrust is the integration of these efforts into a novel Model Zoo to provide robust capabilities for validation, assessment, and translation. This research effort will utilize core scientific innovations from the collaborative team consisting of members from a university (Vanderbilt), a medical center (Vanderbilt Medical Center), two industry partners (MD.ai, Kaggle), and a professional society (SIIM), alongside widely used, open source platforms. In Phase 1 of the Convergence Accelerator, the project will focus on newly created public and private datasets for COVID-19. Phase II will scale this approach to different medical imaging modalities, including dermatology and ophthalmology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.