Maria Tirindelli

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About

Maria Tirindelli is a leading researcher at the intersection of robotics and medical imaging, with a primary focus on autonomous ultrasound-guided robotic navigation. Her most impactful contribution is the development of the first reinforcement learning (RL)-based method for robotic navigation using real-time ultrasound images, introduced in her seminal 2020 paper (50 citations). By combining deep Q-networks with memory buffers and a binary classifier, Tirindelli’s work enables robots to intelligently interpret ultrasound feedback, dramatically improving accuracy and repeatability in clinical procedures. She further advanced the field with her 2022 study on acoustic shadowing-aware robotic ultrasound (9 citations), which addresses a critical limitation of ultrasound imaging—shadow artifacts—by teaching robots to “see” around obstructions, effectively “lighting up the dark” regions. Her research directly tackles the high user-dependency and interpretability challenges that have hindered ultrasound’s broader adoption. Tirindelli’s innovative fusion of deep reinforcement learning and medical robotics has positioned her as a pioneer in autonomous ultrasound systems, with her work laying the groundwork for safer, more reliable, and operator-independent diagnostic and interventional tools.