Artificial intelligence in modern neurosurgery: new perspectives of laminoplasty

INTRODUCTION. The use of laminoplasty is increasingly common in modern clinical practice. Despite the obvious advantages, this operation is fraught with a number of problems, in addition, like any invasive procedure, it is fraught with potential complications. In recent years, revolutionary advances in technology have changed many disciplines and sectors, with medicine, more specifically neurosurgery, becoming the most important area of such innovative metamorphoses. The intersection of artificial intelligence (AI) and neurosurgery heralds a futuristic chapter with unprecedented potential that promises improved surgical practice conditions and patient outcomes. The application of AI to the laminoplasty method can be considered as the basis for the development of this direction.

AIM. To to analyze the possibilities of using AI in laminoplasty, the most important method of decompression of the spinal canal.

MATERIALS AND METHODS. A meta-analysis was carried out on publications on this topic, on English- and Russianlanguage resources: PubMed, Medscape, eLibrary. The search was carried out by keywords: “AI”, “laminoplasty”, “posterior access”, “introduction of AI into neurosurgery”, “cervical spine”. All works were considered in the time range from 2019 to 2023. The number of selected and analyzed works is 68.

RESULTS. The data on the positive outcomes of the use of AI in neurosurgery were revealed, namely, those related to the analysis of instrumental data (MRI), preoperative studies, intraoperative changes and also the direct use of AI in surgical intervention.

CONCLUSION. By integrating AI, we can optimize diagnostic accuracy, surgical planning, and operational accuracy, significantly improving patient outcomes. However, the introduction of AI involves relevant ethical and legal considerations that require careful study.

1. Danilov G. V., Shifrin M. A., Kotik K. V., Ishankulov T. A., Orlov Y. N., Kulikov A. S., Potapov A. A. Artificial intelligence in neurosurgery: A systematic review using topic modeling. Part I: Major research areas. Sovremennye Tehnologii v Medicine. 2020;12(5):106–112. Doi: 10.17691/stm2020.12.5.12.

2. Danilov G. V., Shifrin M. A., Kotik K. V., Ishankulov T. A., Orlov Y. N., Kulikov A. S., Potapov A. A. Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovremennye Tehnologii v Medicine. 2021;12(6):111–118. Doi: 10.17691/stm2020.12.6.12.

3. Jonathan A. Tangsrivimol, Ethan Schonfeld, Michael Zhang, Anand Veeravagu, Timothy R. Smith, Roger Härtl, Michael T. Lawton, Adham H. El-Sherbini, Daniel M. Prevedello, Benjamin S. Glicksberg, Chayakrit Krittanawong. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. PMC: 10378231. Doi: 10.3390/diagnostics13142429.

4. Tabatabaei M., Razaei A., Sarrami A. H., Saadatpour Z., Singhal A., Sotoudeh H. Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review. Oncology. 2021;99(7):433–443. Doi: 10.1159/000515597.

5. Ning Z., Luo J., Xiao Q., Cai L., Chen Y., Yu X., Wang J., Zhang Y. Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. Ann Transl Med. 2021;9(4):298. Doi: 10.21037/atm-20-4076.

6. Jian A., Jang K., Manuguerra M., Liu S., Magnussen J., Di Ieva A. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. Neurosurgery. 2021;89(1):31–44. Doi: 10.1093/neuros/nyab103.

7. D’Este S., Nielsen M., Hansen A. Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature. Diagnostics. 2021;11(4):592. Doi: 10.3390/diagnostics11040592.

8. Van Kempen E., Post M., Mannil M., Witkam R., Ter Laan M., Patel A., Meijer F. J. A., Henssen D. Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and metaanalysis. European Radiology. 2021;31(12):9638–9653. Doi: 10.1007/s00330-021-08035-0.

9. Huang J., Shlobin N. A., Lam S. K., DeCuypere M. Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World Neurosurgery. 2022;(157):99–105.

10. Kocher M., Ruge M. I.., Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlentherapie Und Onkologie. 2020;196(10):856–867. Doi: 10.1007/s00066-020-01626-8.

11. Dinara H. Giniyatova, Vasilii A. Lapinskii. Nodules detection on computer tomograms using neural networks. Program Systems: Theory and Applications. 2022;13(3(54)):81–98. (In Russ.)].

12. Mick J. Perez-Cruet, Robert J. Welsh, Namath S. Hussain, Evan M. Begun, Jules Lin, Paul Park. Use of the da Vinci minimally invasive robotic system for resection of a complicated paraspinal schwannoma with thoracic extension: case report. Doi: 10.1227/NEU.0b013e31826112d8.

13. Danilov G. V., Potapov A. A., Shifrin M. A. et al. Investigation of complications in neurosurgery using artificial intelligence technologies Russian neurosurgical journal named after professor A. L. Polenov. 2022. (In Russ.)].

14. Basov L. A., Karjakin O. E., Martynova N. A., Kochorova L. V. Prediction of postoperative complications based on neural network technologies. (In Russ.)]. Doi: 10.12737/17035.

15. Petukhova N. V., Farkhadov M. P., Zamegrad M. .V, Grachev S. P. Digital technologies in the diagnosis and treatment of neurological diseases. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2019;11(4):104–110. Doi: 10.14412/2074-2711-2019-4-104-110.

16. Petersson L., Larsson I., Nygren J. M. et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022;2(2):850. Doi: 10.1186/s12913-022-08215-8.