A Framework for Synthetic ICH to Assess AI Generalizability

Synthetic intracranial hemorrhage (ICH) datasets and artificial intelligence model files to recreate figures found in Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (under submission) using the Jupyter Notebooks found in the manuscript repository.

This repository contains the dataset used to reproduce the results presented in the paper: Knowledge-based synthetic intracranial hemorrhage CT datasets for device evaluations and generalizability assessments (preprint in Files).

Deep learning models for Computer-Assisted Detection (CAD) of intracranial hemorrhage (ICH) often struggle with generalizability when encountering CT data with characteristics underrepresented in their training sets (e.g., variations in patient demographics, hemorrhage types, or image acquisition parameters).

This project introduces an open-source framework to:

Generate synthetic ICH CT data by inserting realistic, modeled hemorrhages (epidural, subdural, intraparenchymal) into a digital head phantom.

Simulate mass effect and control hemorrhage volume and attenuation based on real data distributions.

Create datasets with varied CT acquisition parameters (mAs, kVp) to robustly evaluate the generalizability of ICH detection models.

Our work validates this approach by demonstrating comparable performance of an ICH detection model on our synthetic dataset (AUC 0.877) versus an independent real dataset (AUC 0.919). This framework enables more comprehensive testing and evaluation of CAD devices for ICH.