Z. Curto
Papers
2
Total Citations
19
H-Index
2
About
Z. Curto is pioneering the integration of artificial intelligence into neurorehabilitation, with a focused mission to personalize recovery for stroke survivors. Her research sits at the intersection of machine learning, robotics, and clinical therapy, specifically developing decision support systems that transform complex data from robotic platforms and serious games into actionable clinical insights. Curto’s most cited work introduces a Random Tree Forest model that predicts optimal rehabilitation parameters, enabling therapists to tailor upper extremity robot-assisted therapy to individual patient needs. A subsequent study extends this framework into a continuous outcome prediction system, addressing two critical clinical challenges: interpreting robotic performance metrics from a motor recovery perspective and optimizing therapy duration. While her citation counts (14 and 5 for her top papers) reflect a rapidly emerging career, the translational impact of her work is significant—offering a data-driven pathway to more effective, customized stroke rehabilitation. By bridging the gap between advanced technology and clinical practice, Curto is laying the groundwork for a future where AI-powered tools become standard in personalized neurorehabilitation.
Research Focus
Key Achievements
Top Papers
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- 2