Women in Data Science Puget Sound 2024

Synthetic data is increasingly important and relevant in today's data-driven landscape. It addresses privacy concerns by providing a means to generate data that mimics real-world information without exposing sensitive personal details. This makes it particularly valuable in fields like healthcare, where data privacy is paramount. Additionally, synthetic data can be used to fill gaps in datasets where real-world data is scarce or biased, enabling more comprehensive and unbiased AI training. It also allows for the testing and validation of systems in a controlled environment, enhancing model robustness and accuracy. Furthermore, synthetic data is instrumental in scenarios where gathering real-world data is impractical or too expensive, thus accelerating research and development across various industries. Our simulators and expertise in surgical simulation enables us to generate synthetic data that significantly enhances AI applications in the medical field. This contribution is pivotal in advancing AI-driven innovations in surgery. By harnessing advanced algorithms and state-of-the-art simulation technologies, we can produce high-quality synthetic data that closely mimics real-world surgical scenarios.

The core of this presentation revolves around the Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS) challenge, hosted at MICCAI 2023 in Vancouver, Canada. The Syn-ISS challenge highlights the innovative use of semantic image segmentation algorithms and synthetic data derived from our state-of-the-art surgical simulators. Specifically, the challenge focuses on segmenting surgical instruments within synthetic data images. We had 12 participating teams compete. The dataset consisted of 3600 synthetic images generated from our FlexVR simulator. The winners were chosen based on a composite score of rankings, based on two weighted metrics: Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD). This challenge and participants showcased that synthetic data can be used in medical AI, benefiting medical education for humans and machines, ultimately improving patient outcomes.