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. 2021;12(6):111-118.
doi: 10.17691/stm2020.12.6.12. Epub 2020 Dec 28.

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

Affiliations

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

G V Danilov et al. Sovrem Tekhnologii Med. 2021.

Abstract

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.

Methods: Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery.

Results: The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined.

Conclusion: The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.

Keywords: artificial intelligence; natural language processing; neurosurgery; topic modeling in neurosurgery.

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Conflict of interest statement

Conflict of interest. There is no conflict of interest to be declared.

References

    1. Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A. PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1. doi: 10.1186/2046-4053-4-1. - DOI - PMC - PubMed
    1. Charron O., Lallement A., Jarnet D., Noblet V., Clavier J.B., Meyer P. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med. 2018;95:43–54. doi: 10.1016/j.compbiomed.2018.02.004. - DOI - PubMed
    1. Chang K., Beers A.L., Bai H.X., Brown J.M., Ly K.I., Li X., Senders J.T., Kavouridis V.K., Boaro A., Su C., Bi W.L., Rapalino O., Liao W., Shen Q., Zhou H., Xiao B., Wang Y., Zhang P.J., Pinho M.C., Wen P.Y., Batchelor T.T., Boxerman J.L., Arnaout O., Rosen B.R., Gerstner E.R., Yang L., Huang R.Y., Kalpathy-Cramer J. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bi-dimensional measurement. Neuro Oncol. 2019;21(11):1412–1422. doi: 10.1093/neuonc/noz106. - DOI - PMC - PubMed
    1. Banzato T., Causin F., Della Puppa A., Cester G., Mazzai L., Zotti A. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study. J Magn Reson Imaging. 2019;50(4):1152–1159. doi: 10.1002/jmri.26723. - DOI - PMC - PubMed
    1. Chen X., Tong Y., Shi Z., Chen H., Yang Z., Wang Y., Chen L., Yu J. Noninvasive molecular diagnosis of craniopharyngioma with MRI-based radiomics approach. BMC Neurol. 2019;19(1):6. doi: 10.1186/s12883-018-1216-z. - DOI - PMC - PubMed

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