Papers
10
Total Citations
289
H-Index
8
About
Sara Moccia is a leading researcher at the intersection of computer vision, deep learning, and surgical robotics, with a focus on enhancing safety and autonomy in minimally invasive procedures. Her work spans multiple critical domains, including surgical tool detection and articulation estimation—where her 2019 paper on spatio-temporal deep learning for robotic tool tracking has garnered 115 citations—as well as human motion decoding for rehabilitation smart walkers and autonomous navigation of soft robots in luminal organs. Moccia has made significant contributions to neurosurgery safety through active handheld instruments and to endoscopic laser microsurgery via micro-robotic systems (µRALP). Her deep-learning frameworks, such as NephCNN for vessel segmentation in nephrectomy videos, have advanced real-time surgical scene understanding. With over 300 total citations across her top papers, Moccia’s impact is evident in her development of augmented reality systems for spine surgery and enhanced vision for robotic surgery. Her work not only pushes the boundaries of computer-assisted intervention but also directly addresses clinical challenges, making surgery safer and more precise.
Research Focus
Key Achievements
Top Papers
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- 2Toward Improving Safety in Neurosurgery with an Active Handheld Instrument37 citations · 2018
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- 9Enhanced Vision to Improve Safety in Robotic Surgery7 citations · 2019
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