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 MATLAB script that automates the determination of stiffness from the slope of a linear region from mechanical test data using an algorithm that is in compliance with ASTM E3076-18 [1]. Specifically, it analyzes test data (i.e., force-displacement curve or torque-angle curve) and then generates output parameters including bending and torsional stiffness typically requested in the preclinical mechanical performance test standards, ASTM F3574 [2] and ASTM F2267 [3].
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 computer model intended to support evaluation of photon counting detectors (PCDs) during the product development phase by generating in-silico X-ray projections of computational anatomical models detected by PCDs.
sFRC (scanning Fourier Ring Correlation) is a tool that compares radiological images from AI or iterative-based image restoration algorithms against those from standard-of-care analytical algorithms to identify and label hallucinations (aka fakes) using small red bounding boxes, which serve as visual indicators of the detected hallucinations.
This regulatory science tool presents a method for assessing credibility of patient-specific computational models implemented in medical device software.