Illustration portraying neurosurgeon’s hands performing microanastomosis under an operating microscope, with continuous tracking of 21 hand landmarks by a supervising machine learning algorithm. Against a backdrop of darkness, an auspicious light radiates at the center, symbolizing the unknown potential that artificial intelligence holds for the future of neurosurgery. Artist: Aaron Cole, MS. Used with permission from Barrow Neurological Institute, Phoenix, Arizona. See the article by Gonzalez-Romo et al. (E2).
Newswise — Rolling Meadows, IL (June 1, 2023). The June issue of Neurosurgical Focus (Vol. 54, No. 6 [https://thejns.org/focus/view/journals/neurosurg-focus/54/6/neurosurg-focus.54.issue-6.xml]) presents sixteen articles on the rising use of machine learning in neurosurgical research and practice.
Topic Editors: Mohamad Bydon, John H. Shin, Shelly D. Timmons, and Eric A. Potts
The June issue of Neurosurgical Focus is focused on new developments that have led to the increased use of machine learning in neurosurgery. In this issue, “the authors study current machine learning models, evaluate novel algorithms, and discuss approaches to appraising machine learning techniques in neurosurgery,” as noted by the Topic Editors, who hope that this issue will contribute to providing “a long-lasting reference for clinicians and scientists, stimulate scientific thought, and provide a framework for a fruitful conversation on a highly complicated and challenging topic.”
Contents of the June issue:
- “Introduction. Machine learning in neurosurgery: transitioning to a new era of contemporary medicine” by Mohamad Bydon et al.
- “Quantification of motion during microvascular anastomosis simulation using machine learning hand detection” by Nicolas I. Gonzalez-Romo et al.
- “Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models” by Sivaram Emani et al.
- “Metabolomic differentiation of tumor core versus edge in glioma” by Mary E. Baxter et al.
- “Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study” by Christine Park et al.
- “Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis” by Haosu Zhang et al.
- “Developing nonlinear k-nearest neighbors classification algorithms to identify patients at high risk of increased length of hospital stay following spine surgery” by Shane Shahrestani et al.
- “Machine learning predictive models in neurosurgery: an appraisal based on the TRIPOD guidelines. Systematic review” by Anmol Warman et al.
- “Incorporation of a biparietal narrowing metric to improve the ability of machine learning models to detect sagittal craniosynostosis with 2D photographs” by Megan G. Anderson et al.
- “Surgical classification using natural language processing of informed consent forms in spine surgery” by Michael D. Shost et al.
- “MRI-based detection of cervical ossification of the posterior longitudinal ligament using a novel automated machine learning diagnostic tool” by Shachar Shemesh et al.
- “Using machine learning to predict 30-day readmission and reoperation following resection of supratentorial high-grade gliomas: an ACS NSQIP study involving 9418 patients” by Abdul Karim Ghaith et al.
- “Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography–digital subtraction angiography image pairs” by Tatsat R. Patel et al.
- “Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury” by Dooman Arefan et al.
- “Immunohistochemical markers predicting long-term recurrence following clival and spinal chordoma resection: a multicenter study” by Abdul Karim Ghaith et al.
- “Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph” by Andrew Abumoussa et al.
- “Radiomic signatures of meningiomas using the Ki-67proliferation index as a prognostic marker of clinical outcomes” by Omaditya Khanna 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] 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 since 1944, 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).
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