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About

Rosa Sicilia is a researcher working at the intersection of medical imaging, radiomics, and rehabilitation robotics. Her primary research areas include the application of radiomics for predicting treatment outcomes in acoustic neuroma (vestibular schwannoma) and the use of deep learning for human locomotion analysis in lower-limb exoskeletons. Sicilia’s major contributions involve pioneering radiomics-based approaches to predict response to CyberKnife stereotactic radiosurgery, with early pilot studies (2018, 4 citations) and subsequent work tackling class imbalance in radiomic feature analysis (2019, 8 citations). These studies aim to improve non-invasive tumour growth control and patient-specific treatment planning. More recently, she has advanced wearable robotics through comparative deep learning studies for adaptive exoskeleton control (2025, 5 citations) and the AI-CARE project (2025, 3 citations), which focuses on customized robot-aided rehabilitation. Her work bridges computational methods with clinical applications, demonstrating impact in both oncology and assistive technology. Sicilia’s research is particularly notable for addressing real-world challenges such as data imbalance in medical datasets and the need for adaptive, personalized rehabilitation systems.