BARDA DRIVe is seeking information on technologies that are relevant to AI for automated biomedical image acquisition and AI for automated biomedical image interpretation, with a focus on reducing the burden on clinical, technician, and care staff in any setting. If you can provide insight on any of the items listed below, please contact Phuong Nguyen (phuong.t.nguyen@stonybrook.edu) by June 8, 2021.
This request encompass all stages of development from early research/academic labs through start up and late commercialization: They are more interested in technical rather than any particular stage of development, thus the focus is not just on the commercial sector but also on the work being conducted at academic/research/medical/FFRDC organizations. This can be an AI solution applied to existing imaging infrastructure, or new imaging devices. This AI and imaging combination should provide new value propositions, form factors, or use cases that are only possible with integration of AI.
Specific areas and use cases of interest include but are not limited to:
– Automated biomedical image interpretation, primarily in ultrasound, computer tomography, and magnetic resonance imaging.
– Approaches to automated interpretation of optical and x-ray images are of interest, but of secondary priority.
– AI-based diagnostic image acquisition and image interpretation in emergency medical services (EMSs) and in surgical settings to accurately assess the severity of a patient’s medical condition, e.g. the location of an internal hemorrhage or the location of a broken bone, torn ligament or other injury.
– AI-based automated image reconstruction.
– AI-based automated analysis of cancer screening images (e.g. mammograms)
– AI-assisted image guidance for placement of medical devices, e.g. catheterization
– AI-assisted fluoroscopy