Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

Background Artificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interventional radiology has the potential to increase the efficacy and cost-efficiency of interventional procedures while improving postsurgical outcomes and reducing the burden for medical personnel. Methods To overcome the lack of available clinical data needed to train state-of-the-art AI models, we propose a novel approach for generating synthetic ultrasound data from real, clinical preoperative three-dimensional (3D) data of different imaging modalities. With the synthetic data, we trained a deep learning-based detection algorithm for the localization of needle tip and target anatomy in US images. We validated our models on real, in vitro US data. Results The resulting models generalize well to unseen synthetic data and experimental in vitro data making the proposed approach a promising method to create AI-based models for applications of needle and target detection in minimally invasive US-guided procedures. Moreover, we show that by one-time calibration of the US and robot coordinate frames, our tracking algorithm can be used to accurately fine-position the robot in reach of the target based on 2D US images alone. Conclusions The proposed data generation approach is sufficient to bridge the simulation-to-real gap and has the potential to overcome data paucity challenges in interventional radiology. The proposed AI-based detection algorithm shows very promising results in terms of accuracy and frame rate. Relevance statement This approach can facilitate the development of next-generation AI algorithms for patient anatomy detection and needle tracking in US and their application to robotics. Key points • AI-based methods show promise for needle and target detection in US-guided interventions. • Publicly available, annotated datasets for training AI models are limited. • Synthetic, clinical-like US data can be generated from magnetic resonance or computed tomography data. • Models trained with synthetic US data generalize well to real in vitro US data. • Target detection with an AI model can be used for fine positioning of the robot. Graphical Abstract