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About

Sarah Latus is a leading researcher at the intersection of surgical robotics, haptic feedback, and medical image analysis, with a primary focus on improving the safety and precision of needle-based interventions. Her work addresses critical challenges in procedures ranging from biopsies to epidural anesthesia, where accurate needle placement is paramount. Latus has pioneered the use of deep learning for advanced sensing and guidance, including the detection of tissue ruptures during needle insertion using complex OCT data and CNNs—a contribution that has garnered 14 citations. She has also developed novel methods for force sensing based on instrument-tissue interaction, enabling haptic feedback in minimally invasive and robotic surgery without costly instrument modifications. Her collaborative robotic biopsy system, which integrates trajectory guidance with needle tip force feedback, represents a significant step toward real-time navigation in clinical settings. More recently, Latus has advanced needle tracking in low-resolution 3D ultrasound volumes using deep learning, addressing a key limitation of conventional 2D imaging. Her work on proximity-based haptic feedback for collaborative robotic needle insertion further demonstrates her commitment to enhancing procedural safety. With a growing citation record and a clear trajectory toward clinical translation, Latus is establishing herself as a key innovator in image-guided, robot-assisted surgery.