Synthetic data-driven AI for pacemaker removal in X-ray angiograms

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. While the most common technique for diagnosing CVDs is X-ray Angiography (XA), the presence of medical devices in angiograms can significantly obstruct the visibility of arterial abnormalities, reducing the efficacy and accuracy of diagnosis. Pacemakers are among the most common of these obstructive devices. Therefore, we propose a state of-the-art deep learning model capable of automatic digital removal of pacemakers from XA images. First, we generate a synthetic dataset of 25 000 training images with and without a pacemaker, using annotated images manually collected and then combined through a derived variation of the Beer-Lambert law. Next, we train our model on the synthetic dataset. To validate our approach, we evaluate our model on synthetic data as well as real data captured from an FDA-approved chest phantom. Despite being trained solely on synthetic data, our model yields an average structural similarity index of 0.98 on our synthetic test set, and 0.95 on our real-world test set. This approach marks the first fully automated solution to pacemaker removal from coronary angiograms. Our work has the potential to enhance the accuracy of CVD diagnoses by providing clearer coronary images, ultimately improving patient outcomes and supporting more effective clinical decision-making.