Regulatory Science Tools Catalog
The Regulatory Science Tools Catalog provides a peer-reviewed resource for use where standards and qualified Medical Device Development Tools (MDDTs) do not yet exist. These tools do not replace FDA-recognized standards or MDDTs. This catalog collates a variety of regulatory science tools that the FDA's Center for Devices and Radiological Health's (CDRH) Office of Science and Engineering Labs (OSEL) developed. If you are considering using a tool from this catalog in your marketing submissions, note that these tools have not been qualified as Medical Device Development Tools and the FDA has not evaluated the suitability of these tools within any specific context of use. You may request feedback or meetings for medical device submissions as part of the Q-Submission Program.
This regulatory science tool (RST) is a computational method that employs conditional denoising diffusion probabilistic model (DDPM) to generate synthetic histopathology images guided by nuclei segmentation masks, helping to augment limited annotated whole slide image (WSI) datasets for AI model development and validation in digital pathology.
DxGoals is a freely-accessible, RShiny software application that is intended to determine and visualize performance goals for common diagnostic test classification accuracy metrics including sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Model outputs are dependent on user inputs of desired risk stratification (pre- and post-test probabilities of the target condition). The tool also analyzes whether goals are met with statistical significance.
- Github Webpage: https://github.com/DIDSR/DxGoals
- Link to Software: https://fda-cdrh-osel-didsr-rst.shinyapps.io/DxGoals/
This regulatory science tool (RST) is a software program written in Python for performance assessment of segmentation algorithms applied to digital pathology whole slide images (WSIs).
This regulatory science tool is a software program written in Python for analyzing whole slide images (WSIs) and assisting developers or pathologists in the assessment of machine learning algorithms used in digital pathology.