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New Technologies in Neurosurgery: An Insight into the Future
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New Technologies in Neurosurgery: An Insight into the Future

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: 19 January 2026 | Viewed by 587

Special Issue Editor

Dr. Ismail Zaed

E-Mail Website
Guest Editor
Department of Neurosurgery, Neurocenter of the Southern Switzerland, Regional Hospital of Lugano, Lugano, Switzerland.
Interests: neurosurgery; spine surgery; medical education; pediatric neurosurgery; functional neurosurgery; endoscopic neurosurgery; neuroanatomy; vascular neurosurgery

Special Issue Information

Dear Colleagues,

This Special Issue titled "New Technologies in Neurosurgery: An Insight into the Future" invites researchers, clinicians, and experts in the field of neurosurgery to contribute original research and review articles exploring the cutting-edge technologies transforming neurosurgical practices. The focus is on innovative advancements that have the potential to revolutionize diagnosis, treatment, and patient outcomes in neurosurgery.

The key areas of interest for this Special Issue include, but are not limited to, the integration of artificial intelligence and machine learning into surgical planning, the use of robotics for precision surgeries, advancements in neuroimaging techniques, and the application of augmented and virtual reality in surgical training. The aim is to provide a platform for sharing pioneering research that addresses current challenges and explores future possibilities in neurosurgical technology.

This call for papers encourages submissions that highlight interdisciplinary approaches and clinical trials demonstrating the impact of these technologies on patient care. Submissions should offer insightful discussions on the implications of these advancements for the future of neurosurgical practice, including ethical considerations and potential barriers to their widespread adoption. Accepted papers will contribute to a Special Issue dedicated to the future trajectory of neurosurgery, offering a comprehensive overview of how emerging technologies can enhance surgical outcomes and patients’ quality of life.

Dr. Ismail Zaed
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neurosurgery
  • training
  • technology
  • augmented reality
  • virtual reality
  • robot

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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13 pages, 795 KiB  
Systematic Review
Publicly Available Datasets for Artificial Intelligence in Neurosurgery: A Systematic Review
by Bianca Chan, Brandon Kim, Ethan Schonfeld, George Nageeb, Aaradhya Pant, Adam Sjoholm, Ravi Medikonda, Ummey Hani and Anand Veeravagu
J. Clin. Med. 2025, 14(16), 5674; https://doi.org/10.3390/jcm14165674 - 11 Aug 2025
Viewed by 138
Abstract
Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, labeled datasets. Labeled neurosurgical datasets are rare, driven by the high expertise required for labeling neurosurgical data. A comprehensive resource overviewing available datasets for AI in neurosurgery [...] Read more.
Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, labeled datasets. Labeled neurosurgical datasets are rare, driven by the high expertise required for labeling neurosurgical data. A comprehensive resource overviewing available datasets for AI in neurosurgery is essential to identify areas for potential model building and areas of needed data construction. Methods: We conducted a systematic review according to PRISMA guidelines to identify publicly available neurosurgical datasets suitable for machine learning. A PubMed search on 8 February 2025, yielded 267 articles, of which 86 met inclusion criteria. Each study was reviewed to extract dataset characteristics, model development details, validation status, availability, and citation impact. Results: Among the 86 included studies, 83.7% focused on spine pathology, with tumor (3.5%), vascular (4.7%), and trauma (7.0%) comprising the remaining. The majority of datasets were image-based, particularly X-ray (37.2%), MRI (29.1%), and CT (20.9%). Label types included segmentation (36.0%), diagnosis (26.7%), and detection/localization (20.9%), with only 2.3% including outcome labels. While 97.7% of studies reported training a model, only 22.6% performed external validation, 20.2% shared code, and just 7.1% provided public applications. Accuracy was the most frequently reported performance metric, even for segmentation tasks, where only 60% of studies used the Dice score metric. Studies often lacked task-appropriate evaluation metrics. Conclusions: We conducted a systematic review to capture all publicly accessible datasets that can be applied to build AI models for neurosurgery. Current datasets are heavily skewed towards spine imaging and lack both clinical patient specific and outcomes information. Provided baseline models from these datasets are limited by poor external validation, lack of reproducibility, and reliance on suboptimal evaluation metrics. Future efforts should prioritize developing multi-institutional datasets with outcome labels, validated models, public access, and domain diversity to accelerate the safe and effective integration of AI into neurosurgical care. Full article
(This article belongs to the Special Issue New Technologies in Neurosurgery: An Insight into the Future)
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