Guest Editor: Dr. Francesca Faedo
Email: [email protected]
Affiliation: Humanitas University, Pieve Emanuele, Italy
Research interest: Spine surgery; Spinal surgery
Guest Editor: Prof. Dr. Delia Cannizzaro
Email: [email protected]
Affiliation: Humanitas Research Hospital, Rozzano, Italy
Research interest: Spine surgery; Spinal surgery; Spinal cord injury; Neurosurgery
Submission Deadline: 11 August 2025
Note: When submitting your manuscript to a special issue, please identify the special issue's name on top of the title.
Special Issue Information：
Dear Colleagues,
Clinical engineering is a branch of biomedical engineering that focuses on applying medical technology to improve the efficiency and quality of healthcare delivery. It has already been successfully implemented in various medical fields, where it plays a crucial role in enhancing patient outcomes. In neurosurgery, for instance, several of these tools have been employed to improve surgical precision. For example, clinical engineering has contributed to the development of advanced visualization systems which go beyond the traditional microscope, such as the exoscope. It is also responsible for innovations in prosthetic materials and implants used in cranioplasty, as well as technologies like neuronavigation tools for accurate surgical guidance. While the application of robotic surgery in neurosurgery has been limited thus far, it remains a promising area for further exploration. Additionally, it is intriguing to consider how artificial neural networks and machine learning might be applied in neurosurgery, given their success in other fields such as radiology, oncology, and genomics, where they have led to improved patient outcomes. In this special issue, we aim to explore how new clinical engineering tools can be applied in neurosurgery, or how existing ones can be innovated, with the goal of improving patient outcomes.
Dr. Francesca Faedo
Prof. Dr. Delia Cannizzaro
Guest Editors
Keywords: Clinical engineering; Biomedical engineering; Medical technology; Patient outcomes; Neurosurgery; Visualization systems; Prosthetic materials; Neuronavigation; Robotic surgery; Artificial neural networks; Machine learning
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