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About

Maria Antico is a pioneering researcher at the intersection of medical imaging, deep learning, and surgical robotics, with a particular focus on ultrasound-guided autonomous systems for minimally invasive procedures. Her work has centered on one of orthopedic surgery's most pressing challenges: developing intelligent robotic platforms for knee arthroscopy, where real-time imaging guidance is critical for patient safety and surgical precision. Antico's most influential contribution — her 2019 paper on ultrasound guidance in minimally invasive robotic procedures (69 citations) — established a foundational framework for integrating imaging into robotic surgery workflows. Complementing this, her deep learning-based approaches to femoral cartilage segmentation using architectures such as Mask R-CNN and Bayesian CNNs have advanced the field's ability to automatically delineate delicate anatomical structures from inherently noisy ultrasound data. Notably, her Bayesian approach introduced uncertainty quantification into segmentation, addressing a critical need for reliability in clinical applications. With over 200 cumulative citations across her most impactful works — many published within a remarkably concentrated period between 2019 and 2020 — Antico has rapidly established herself as a leading voice in computer-assisted orthopedic surgery, offering innovations that bring autonomous robotic arthroscopy meaningfully closer to clinical reality.