Artificial Intelligence Innovations in Cerebrovascular Neurosurgery: A Systematic Review of Cutting-edge Applications

Author:

Roy Poulami1,Godbole Aditya Amit2,Banjan Tanvi3,Ahmed Komal Fatima4,Bakhtyar Khadija5,Tendulkar Mugdha6,Ghosh Shankhaneel7,Deb Novonil1,Ali Syed Roshan8,Das Soumyajit7,Tendulkar Reshma9,Lathwal Yashdeep10,Senthil Arihant10,Khullar Kaarvi11,Shree Anagha12,Kundu Mrinmoy7

Affiliation:

1. North Bengal Medical College and Hospital

2. Bharati Vidyapeeth Deemed University

3. Grant Medical College and Sir Jamshedjee Jeejeebhoy Group of Hospitals

4. Ziauddin Medical College

5. Dow Medical College

6. K J Somaiya Medical College

7. Institute of Medical Sciences and Sum Hospital

8. Midnapore Medical College and Hospital

9. Vivekanand Education Society's College of Pharmacy

10. University College of Medical Sciences

11. Government Medical College

12. SGT Medical College Hospital & Research Institute

Abstract

Abstract

Introduction: Artificial Intelligence (AI) offers transformative potential for cerebrovascular neurosurgery, enabling novelapproaches to predict clinical outcomes, enhance diagnostic accuracy, and optimize surgical procedures. Thissystematic review explores AI's integration and impact in this specialized field, emphasizing improved patient careand addressing challenges in resource-limited settings. Methods: A comprehensive literature search across PubMed, Scopus, and Web of Science gathered peer-reviewed articlesdetailing AI applications in cerebrovascular neurosurgery. Studies were analyzed to evaluate AI effectiveness inpredicting outcomes, enhancing detection/diagnosis, facilitating risk stratification, and integrating into surgicalworkflows. Ethical implications and applicability in resource-limited settings were examined. Results: AI demonstrates significant potential in predicting clinical outcomes by analyzing large datasets to forecast patienttrajectories. In detection and diagnosis, AI algorithms utilizing machine learning show high accuracy in interpretingneuroimaging data, enabling earlier and more precise cerebrovascular condition diagnoses. Risk stratification isenhanced through AI's ability to classify patients based on risk profiles, enabling better resource allocation andtargeted interventions. However, AI integration into surgical workflows requires substantial adjustments. Conclusion: AI in cerebrovascular neurosurgery presents a promising avenue for advancing patient care through personalizedtreatment strategies and improved diagnostic and predictive accuracy. Ethical considerations regarding data privacy,algorithmic fairness, and equitable distribution must be rigorously addressed. Future research should focus onovercoming challenges, enhancing clinical workflow integration, and ensuring accessibility across diversehealthcare settings. Continuous collaboration between engineers, clinicians, and ethicists is advocated to fosterinnovative and ethical AI applications in neurosurgery.

Publisher

Research Square Platform LLC

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4. Senders JT et al (Jan. 2018) Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurg 109:476–486. 10.1016/j.wneu.2017.09.149. .e1

5. Obermeyer Z, Emanuel EJ (2016) Predicting the Future — Big Data, Machine Learning, and Clinical Medicine, N Engl J Med, vol. 375, no. 13, pp. 1216–1219, Sep. 10.1056/NEJMp1606181

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