Newswise – Rolling Meadows, Illinois (July 1, 2025). July issue of Neurosurgical focus (Vol. 59, No. 1) Introducing 17 articles on artificial intelligence in neurosurgery.
Topic Editor: Benjamin S. Hopkins, Garnett R. Sutherland, Samuel R. Browd, Daniel A. Donojo, Eric K. Orman, Clemens M. Silmer, Brenton Penisuk, Wale F. Assard
From the topic editor introduction: “For neurosurgeons, AI promises advancements in multiple domains, including operating rooms and hospital efficiency, learning, education, performance, and new tools and technologies. However, the use of AI in healthcare poses challenges such as ethical considerations, data security, and regulatory compliance.”
July issue contents:
- “Introduction. Artificial Intelligence in Neurosurgery: A Data-Intensive Change in Specialty” Benjamin S. Hopkins et al.
- “Streamlined Microsurgical Procedure: Phantom Testing of an Artificial Intelligent Driven Robotic Microscope Assistant” By Michael Murek et al.
- “Phenotypic-Driven Risk Stratification of Cerebral Aneurysms Using Shapley Additive Description – Monitored Clustering: A New Approach to Rupture Prediction” Shrinit Babel and Syed Rh Peeran
- “Definition of cervical spinal myelopathy using co-morbidity clustering: A quality outcome database cervical spinal myelopathy study” Eunice Yang et al.
- “Deep learning-based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning” Zhichao Gan et al.
- “New deep learning system for automatic diagnosis and grading of lumbar spinal stenosis based on spine MRI: Model development and verification” Tianyi Wang et al.
- “A multi-faceted multimodal machine learning model for predicting surgery outcomes for symptomatic hemorrhagic brainstem cavernomalous malformations” Xuchen Dong et al.
- “Using oral statements based on generator artificial intelligence in neurosurgical practices: a pilot study.” Benjamin S. Hopkins et al.
- “Motion analysis of lumbar pedicle screw placement using an artificial intelligence framework” Christian J. Quinones et al.
- “A supervised machine learning approach to predict the need for postoperative interventions in terminal statistics.” Yuki Shinya et al.
- “Imag-based detection transphenoid surgery of endoscopic carotid arteries and sales of Tulsica” Thara Tunthanathip et al.
- “Open Source AI – Fusion of Auxiliary Rapid 3D Color Multimodal Images and Augmented Reality Planning for Preoperative Augmented Reality Brain Tumors” Xiaolin Hou et al.
- “Generating synthetic CT-like imaging of spine from complex radiographs: A comparison of different deep learning architectures” Massimo Bottini et al.
- “Localization and trajectory planning of fully automatic anatomical landmarks for navigated external ventricular drain placement.” Mathijs de Boer et al.
- “Aligning alone predicts mechanical complications after spinal cord deformation surgery in adults? Comparison of alignment, bone quality, and soft tissue machine learning.” Sameer Sundrani et al.
- “Machine learning approach to predict long-term stay after lumbar fusion surgery in patients aged 75 and older: a retrospective cohort study based on a comprehensive geriatric assessment.” Qijun Wang et al.
- “Synthetic neurosurgical data generation with generative adversarial networks and large-scale language models: A study on fidelity, utility and privacy.” Austin A. Bar et al.
- “Zero-shot segmentation of spinal vertebrae with metastatic lesions: analysis model 2 of meta segments and factors affecting unlearned segmentation.” Rushmin Khazanchi et al.
Read this month's issue Neurosurgical focus.
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Embargoed Article Access and Author/Expert Interviews: Contact the publication's JNSPG Director Gillian Shasby [email protected] To arrange interviews with authors and external experts who can access in advance and provide the context for this study.
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Since 1944, the global leader in cutting-edge neurosurgery research. Journal of Neurosurgery (www.thejns.org) is the official journal of the American Association of Neurosurgeons (AANS), representing more than 12,000 members (www.aans.org) around the world.
