Literature DB >> 28481395

A Systematic Review on Machine Learning in Neurosurgery: The Future of Decision-Making in Patient Care.

Emrah Celtikci1.   

Abstract

Current practice of neurosurgery depends on clinical practice guidelines and evidence-based research publications that derive results using statistical methods. However, statistical analysis methods have some limitations such as the inability to analyze nonlinear variables, requiring setting a level of significance, being impractical for analyzing large amounts of data and the possibility of human bias. Machine learning is an emerging method for analyzing massive amounts of complex data which relies on algorithms that allow computers to learn and make accurate predictions. During the past decade, machine learning has been increasingly implemented in medical research as well as neurosurgical publications. This systematical review aimed to assemble the current neurosurgical literature that machine learning has been utilized, and to inform neurosurgeons on this novel method of data analysis.

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Year:  2018        PMID: 28481395     DOI: 10.5137/1019-5149.JTN.20059-17.1

Source DB:  PubMed          Journal:  Turk Neurosurg        ISSN: 1019-5149            Impact factor:   1.003


  10 in total

Review 1.  Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.

Authors:  Mason English; Chitra Kumar; Bonnie Legg Ditterline; Doniel Drazin; Nicholas Dietz
Journal:  Acta Neurochir Suppl       Date:  2022

2.  Foundations of Bayesian Learning in Clinical Neuroscience.

Authors:  Gustav Burström; Erik Edström; Adrian Elmi-Terander
Journal:  Acta Neurochir Suppl       Date:  2022

Review 3.  Artificial intelligence in dermatology and healthcare: An overview.

Authors:  Varadraj Vasant Pai; Rohini Bhat Pai
Journal:  Indian J Dermatol Venereol Leprol       Date:  2021 [SEASON]       Impact factor: 2.545

Review 4.  Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.

Authors:  Amina Adadi; Safae Adadi; Mohammed Berrada
Journal:  Adv Bioinformatics       Date:  2019-04-02

5.  The applications of machine learning in plastic and reconstructive surgery: protocol of a systematic review.

Authors:  Angelos Mantelakis; Ankur Khajuria
Journal:  Syst Rev       Date:  2020-02-28

Review 6.  Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.

Authors:  G V Danilov; M A Shifrin; K V Kotik; T A Ishankulov; Yu N Orlov; A S Kulikov; A A Potapov
Journal:  Sovrem Tekhnologii Med       Date:  2020-10-28

7.  Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning.

Authors:  Tadashi Miyagawa; Minami Sasaki; Akira Yamaura
Journal:  PLoS One       Date:  2020-10-21       Impact factor: 3.240

8.  Machine learning in neurosurgery: a global survey.

Authors:  Victor E Staartjes; Vittorio Stumpo; Julius M Kernbach; Anita M Klukowska; Pravesh S Gadjradj; Marc L Schröder; Anand Veeravagu; Martin N Stienen; Christiaan H B van Niftrik; Carlo Serra; Luca Regli
Journal:  Acta Neurochir (Wien)       Date:  2020-08-18       Impact factor: 2.216

Review 9.  Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.

Authors:  Ishaan Ashwini Tewarie; Joeky T Senders; Stijn Kremer; Sharmila Devi; William B Gormley; Omar Arnaout; Timothy R Smith; Marike L D Broekman
Journal:  Neurosurg Rev       Date:  2020-11-06       Impact factor: 3.042

10.  Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery.

Authors:  Angelos Mantelakis; Yannis Assael; Parviz Sorooshian; Ankur Khajuria
Journal:  Plast Reconstr Surg Glob Open       Date:  2021-06-24
  10 in total

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