Impact of Machine Learning in Neurosurgery: A Systematic Review of Related Literature
DOI:
https://doi.org/10.18034/mjmbr.v8i1.520Keywords:
Machine Learning, artificial intelligence, Neurosurgery, telesurgery, robotics Clinical setupsAbstract
Machine learning is a domain within artificial intelligence that allows for computer algorithms to be learned from experience without them having being programmed. The objective of this study is to summarize the neurosurgical applications of machine learning when compared to clinical expertise. This study uses a systematic search to review articles from the PubMed and Embase databases in comparing various machine learning studies approaches to that of the clinical experts. For this study, 23 studies were identified which used machine learning algorithms for the diagnosis, pre-surgical planning, and outcome prediction. In conclusion, this study identifies that machine learning models can augment decision-making capacity for the surgeons and clinicians in neurosurgical applications. Despite this, there still exist hurdles that involve creation, validation, and the deployment of the machine learning techniques in clinical settings.
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Azimi, P., Mohammadi, H. R, Benzel, E. C., Shahzadi, S., Azhari, S., Montazeri, A. (2015). Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry, 86(3), 251-256.
Campillo-Gimenez, B., Garcelon, N., Jarno, P., Chapplain, J. M., Cuggia, M. (2013). Fulltext automated detection of surgical site infections secondary to neurosurgery in Rennes, France. Stud Health Technol Inform, 192, 572-575.
Chan, T., & Huang, H. K. (2008). Effect of a computer-aided diagnosis system on clinicians’ performance in detection of small acute intracranial hemorrhage on computed tomography. Acad Radiol. 15(3), 290-299.
Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930.
Donepudi, P. K. (2020). Crowdsourced Software Testing: A Timely Opportunity. Engineering International, 8(1), 25-30. https://doi.org/10.18034/ei.v8i1.491
Donepudi, P. K., Ahmed, A. A. A., Saha, S. (2020). Emerging Market Economy (EME) and Artificial Intelligence (AI): Consequences for the Future of Jobs. Palarch’s Journal of Archaeology of Egypt/Egyptology, 17(6), 5562-5574. https://archives.palarch.nl/index.php/jae/article/view/1829
Donepudi, P. K., Banu, M. H., Khan, W., Neogy, T. K., Asadullah, ABM., Ahmed, A. A. A. (2020). Artificial Intelligence and Machine Learning in Treasury Management: A Systematic Literature Review. International Journal of Management, 11(11), 13-22.
Emblem, K. E., Pinho, M. C., Zollner, F. G. (2015). A generic support vector machine model for preoperative glioma survival associations. Radiology, 275(1), 228-234.
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.
Juntu, J., Sijbers, J., De Backer, S., Rajan, J., Van Dyck, D. (2010). Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging, 31(3), 680-689.
Kassahun, Y., Perrone, R., De Momi, E. (2014). Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artif Intell Med, 61(2), 79-88.
Kitajima, M., Hirai, T., Katsuragawa, S. (2009). Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists’ diagnosis performance. Acad Radiol. 2009, 16(3), 313-320.
Liu, K., Hogan, W. R., Crowley, R. S. (2011). Natural Language Processing methods and systems for biomedical ontology learning. J Biomed Inform, 44(1), 163-179.
Mitchell, T. J., Hacker, C. D., Breshears, J. D. (2013). A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging. Neurosurgery, 73(6), 969-982.
Nadkarni, P. M., Ohno-Machado, L., Chapman, W. W. (2011). Natural language processing: an introduction. J Am Med Inform Assoc. 18(5), 544-551.
Nucci, C. G., De Bonis, P., Mangiola, A. (2016). Intracranial pressure wave morphological classification: automated analysis and clinical validation. Acta Neurochir (Wien), 158(3), 581-588.
Rayfield, C. & Swanson, K. (2015). Predicting the response to treatment in GBM: Machine learning on clinical images. Neuro-oncology, 17(167).
Rodrigues, J. F., Paulovich F. V., de Oliveira, M.C., de Oliveira, O. N. (2016). On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis. Nanomedicine (Lond), 11(8), 959-982.
Sinha, M., Kennedy, C. S., Ramundo, M. L. (2001). Artificial neural network predicts CT scan abnormalities in pediatric patients with closed head injury. J Trauma, 50(2), 308-312. https://doi.org/10.1097/00005373-200102000-00018
Zhang, B., Chang, K., Ramkissoon, S. (2017). Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro-oncology, 19(1), 109-117.
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