April 2022 Issue of Neurosurgical Focus: “Big Data and New Technologies in Neurosurgical Decision-Making”

Newswise — Charlottesville, VA (April 1, 2022). The April issue of Neurosurgical Focus (Vol. 52, No. 4 [https://thejns.org/focus/view/journals/neurosurg-focus-video/52/4/neurosurg-focus.52.issue-4.xml]) presents 11 articles and 1 editorial on Big Data and new technologies in neurosurgical decision-making.

 Topic Editors: Michael Y. Wang, Jang W. Yoon, Gelareh Zadeh, Paul Park, Erica F. Bisson, and Daniel M. Sciubba

           As the Topic Editors discuss in their introduction for this issue, “We have tried to capture just some of the early developments in this field across a multiplicity of subspecialties, data use approaches, and applications . . . This issue contains contributions on the predictive modeling of seizures after tumor resection management, machine learning to harvest data from MRI for normal pressure hydrocephalus, planning the ideal pedicle screws based on bone mineral density mapping, and smartphone analytics for “fingerprinting” recovery after surgery, just to name a few.”

 Contents of the April issue:

• “Introduction: Big data and its impact on the future of neurosurgery” by Michael Y. Wang et al.
• “Editorial: The use of big data for improving understanding of the natural history of neurosurgical disease” by Katherine G. Holste et al.
• “An integrated risk model stratifying seizure risk following brain tumor resection among seizure-naive patients without antiepileptic prophylaxis” by Michael C. Jin et al.
• “Data-driven phenotyping of preoperative functional decline patterns in patients undergoing lumbar decompression and lumbar fusion using smartphone
• accelerometry” by Hasan S. Ahmad et al.
• “Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm” by Sophia A. Doerr et al.
• “Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach” by Aleš Vlasák et al.
• “Differentiation of lumbar disc herniation and lumbar spinal stenosis using natural language processing–based machine learning based on positive symptoms” by GuanRui Ren et al.
• “Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning” by Ron Gadot et al.
• “Decision tree–based machine learning analysis of intraoperative vasopressor use to optimize neurological improvement in acute spinal cord injury” by Nitin Agarwal et al.
• “A novel surgical planning system using an AI model to optimize planning of pedicle screw trajectories with highest bone mineral density and strongest pull-out force” by Chi Ma et al.
• “Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study” by Vyom Unadkat et al.
• “Machine learning to predict passenger mortality and hospital length of stay following motor vehicle collision” by John Paul G. Kolcun et al.
• “Development and validation of a novel survival prediction model for newly diagnosed lower-grade gliomas” by Qiang Zhu et al.

Please join us in reading this month’s issue of Neurosurgical Focus.

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Embargoed Article Access and Author/Expert Interviews: Contact JNSPG Director of Publications Gillian Shasby at [email protected] or 434-924-5555 for advance access and to arrange interviews with the authors and external experts who can provide context for this research.

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The global leader for cutting-edge neurosurgery research for more than 75 years, the Journal of Neurosurgery (www.thejns.org) is the official journal of the American Association of Neurological Surgeons (AANS) representing over 12,000 members worldwide (www.AANS.org).