U.S. Food and Drug Administration

This regulatory science tool is an open-access dataset containing raw inertial measurement unit (IMU) and sensorized insole data from individuals with Parkinson’s Disease (PD) and age-matched controls synchronized to a gait walkway reference system that can serve as a tool to assess novel algorithms deriving gait metrics through wearables and explore measurable connections between gait, PD severity, PD progression, and controls, thus leading to future clinical enhancements and treatments for patients.

This regulatory science tool (RST) serves as a reference dataset summarizing the engineering characteristics for orthopedic bone fixation devices (plates and screws) in pediatric patients.

This regulatory science tool presents a method (DRAGen) that identifies the types of errors which Artificial Intelligence (AI) / Machine Learning (ML) medical image classification algorithms might make when used on new populations.

This regulatory science tool presents two methods to amplify Artificial Intelligence (AI) / Machine Learning (ML) model bias to enable the evaluation of bias mitigation methods on models with varying amounts of performance bias.

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 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.

This regulatory science tool is a Python toolkit for calculating the radiation deposited dose for breast computed tomography (CT). It features a GUI that estimates the mean glandular dose based on various breast models, incident spectrum, and measured air kerma.

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 mathematical model of the cardiovascular system response to fluid perturbations and includes a cohort generation tool to simulate virtual subjects as part of non-clinical testing of physiologic closed-loop control algorithms that automate fluid infusions following blood loss.

DomID is a Python package offering a suite of unsupervised deep learning algorithms specifically designed for clustering medical image datasets. The primary goal is to identify subgroups that have not been previously annotated in a given image dataset.