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NSF Convergence Accelerator Track D: Scalable, TRaceable AI for Imaging Translation
Title:
NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3)
Project Number:
2040462
Contact PI/Project Lead:
Bennett Landman
Award Organization:
National Science Federation
Abstract:
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.