The Role of Artificial Intelligence in Diagnostic Neurosurgery: A Systematic Review

The Role of Artificial Intelligence in Diagnostic Neurosurgery: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review The Role of Artificial Intelligence in Diagnostic Neurosurgery: A Systematic Review William Li, Armand Gumera, Shrushti Surya, Alex Edwards, Farynaz Basiri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5922236/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Neurosurgical Review → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making in neuro-oncology, vascular, functional, and spinal subspecialties. Despite its potential, variability in outcomes necessitates a systematic review of its performance and applicability. Methods : A comprehensive search of PubMed, Cochrane Library, Embase, CNKI, and ClinicalTrials.gov was conducted from January 2020 to January 2025. Inclusion criteria comprised studies utilizing AI for diagnostic neurosurgery, reporting quantitative performance metrics. Studies were excluded if they focused on non-human subjects, lacked clear performance metrics, or if they did not directly relate to AI applications in diagnostic neurosurgery. Risk of bias was assessed using the PROBAST tool. This study is registered on PROSPERO, number CRD42025631040 on January 26 th , 2025. Results : Within the 186 studies, neural networks (29%) and hybrid models (49%) dominated. Studies were categorised into neuro-oncology (52.69%), vascular neurosurgery (19.89%), functional neurosurgery (16.67%), and spinal neurosurgery (11.83%). Median accuracies exceeded 85% in most categories, with neuro-oncology achieving high diagnostic accuracy for tumour detection, grading, and segmentation. Vascular neurosurgery models excelled in stroke and intracranial haemorrhage detection, with median AUC values of 97%. Functional and spinal applications showed promising results, though variability in sensitivity and specificity underscores the need for standardised datasets and validation. Discussion: The review’s limitations include the lack of data weighting, absence of meta-analysis, limited data collection timeframe, variability in study quality, and risk of bias in some studies. Conclusion: AI in neurosurgery shows potential for improving diagnostic accuracy across neurosurgical domains. Models used for stroke, ICH, aneurysm detection, and functional conditions such as Parkinson’s disease and epilepsy demonstrate promising results. However, variability in sensitivity, specificity, and AUC values across studies underscores the need for further research and model refinement to ensure clinical viability and effectiveness. Neurosurgery AI diagnosis machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files FunctionalPapersOnly.csv AllOncologyPapers.csv SpinalPapersOnly.csv AllVascularPapers.csv Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2025 Read the published version in Neurosurgical Review → Version 1 posted Editorial decision: Revision requested 18 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 11 Mar, 2025 Reviews received at journal 11 Mar, 2025 Reviewers agreed at journal 11 Mar, 2025 Reviewers invited by journal 11 Mar, 2025 Submission checks completed at journal 07 Mar, 2025 First submitted to journal 28 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5922236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":430674237,"identity":"7131050d-9b6a-4de3-b766-11ba0e05af00","order_by":0,"name":"William 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Despite its potential, variability in outcomes necessitates a systematic review of its performance and applicability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A comprehensive search of PubMed, Cochrane Library, Embase, CNKI, and ClinicalTrials.gov was conducted from January 2020 to January 2025. Inclusion criteria comprised studies utilizing AI for diagnostic neurosurgery, reporting quantitative performance metrics. Studies were excluded if they focused on non-human subjects, lacked clear performance metrics, or if they did not directly relate to AI applications in diagnostic neurosurgery. Risk of bias was assessed using the PROBAST tool. This study is registered on PROSPERO, number CRD42025631040 on January 26\u003csup\u003eth\u003c/sup\u003e, 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Within the 186 studies, neural networks (29%) and hybrid models (49%) dominated. 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