1. Daniels C. J., Wakefield P. J., Bub G. A., Toombs J. D. A narrative review of lumbar fusion surgery with relevance to chiropractic practice. Journal of Chiropractic Medicine, 2016, vol. 15, no. 4, pp. 259‒271. https://doi.org/10.1016/j.jcm.2016.08.007
2. Reid P. C., Morr S., Kaiser M. G. State of the union: a review of lumbar fusion indications and techniques for degenerative spine disease: JNSPG 75th anniversary invited review article. Journal of Neurosurgery: Spine, 2019, vol. 31, no. 1, pp. 1‒14. https://doi.org/10.3171/2019.4.SPINE18915
3. Okoneshnikova A. K. Choice of tactics of surgical treatment patients with degenerative diseases of the lower lumbar spine, taking into account the individual parameters of the facet joints. Ph. D. Thesis. Novosibirisk, 2019. 187 p.
4. Mobbs R. J., Phan K., Malham G., Seex K., Rao P. J. Lumbar interbody fusion: techniques, indications and comparison of interbody fusion options including PLIF, TLIF, MI-TLIF, OLIF/ATP, LLIF and ALIF. Journal of Spine Surgery, 2015, vol. 31, no. 1, pp. 2-18. https://doi.org/10.3978/j.issn.2414-469X.2015.10.05
5. Dijkerman M. L., Overdevest G. M., Moojen W. A., Vleggeert-Lankamp C. L. Decompression with or without concomitant fusion in lumbar stenosis due to degenerative spondylolisthesis: a systematic review. European Spine Journal, 2018, vol. 31, pp. 1629-1643. https://doi.org/10.1007/s00586-017-5436-5
6. Borshhenko I. A., Borshchenko Ja. A., Baskov A. V. The use of modern methods of mathematical data mining to obtain an algorithm for minimally invasive surgical treatment of degenerative diseases of the lumbar spine. Vertebrologiya v Rossii: itogi i perspektivy razvitiya: V s’’ezd khirurgov-vertebrologov Rossii (23-24 maya 2014 goda, Saratov): sbornik materialov [Vertebrology in Russia: results and development prospects: V congress of vertebrological surgeons of Russia (May 23-24, 2014, Saratov): collection of materials]. Saratov, 2014, pp. 38‒40 (in Russian).
7. Komleva N. E., Daurov S. K., Bol’shakov A. A., Glazkov V. P., Bakutkin V. V., Mar’yanovskii A. A. Computer programme for analyzing digital magnetic resonance tomography of lumbosacral part of spine. Vestnik novykh meditsinskikh tekhnologii [Bulletin of new medical technologies], 2012, vol. 19, no. 1, pp. 192‒195 (in Russian).
8. Tariciotti L., Palmisciano P., Giordano M., Remoli G., Lacorte E., Bertani G., Locatelli M., Dimeco F., Caccavella V. M., Prada F. Artificial intelligence-enhanced intraoperative neurosurgical workflow: current knowledge and future perspectives. Journal of Neurosurgical Sciences, 2022, vol. 66, no. 2, pp. 139-150. https://doi.org/10.23736/S0390-5616.21.05483-7
9. Buchlak Q. D., Esmaili N., Leveque J.-C., Farrokhi F., Bennett C., Piccardi M., Sethi R. K. Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review. Neurosurgical Review, 2020, vol. 43, no. 5, pp. 1235-1253. https://doi.org/10.1007/s10143-019-01163-8
10. Remov P. S., Mazurenko A. N., Makarevich S. V. Determination of surgical tactics for lumbar dorsopathies using software. Traumatology and Orthopaedics of Kazakhstan, 2021, no. 58 (Special issue), pp. 53-55 (in Russian).
11. Remov P. S., Mazurenko A. N., Makarevich S. V., Chumak N. A., Pustovoitov K. V. Comparative analysis of long-term results of surgical treatment of degenerative spondylolisthesis at the lumbar level. Zdravookhranenie [Healthcare], 2022, no. 9, pp. 51-58 (in Russian).
12. Kovalev E. V., Kirilenko S. I., Mazurenko A. N., Filyustin A. E., Dubrovskii V. V. Smartphone-assisted augmented reality technology for preoperative planning in spinal surgery. Khirurgiya pozvonochnika [Spine surgery], 2021, vol. 18, no. 3, pp. 94-99 (in Russian).
13. Lee S. H., Bae J. S. Comparison of clinical and radiological outcomes after automated open lumbar discectomy and conventional microdiscectomy: a prospective randomized trial. International Journal of Clinical and Experimental Medicine, 2015, vol. 8, no. 8, pp. 12135-12148.
14. Gorbachev S. V., Syryamkin V. I. Neuro-fuzzy methods in intelligent systems of processing and analysis of multidimensional information. Tomsk, Tomsk University Publishing House, 2014. 442 p. (in Russian).
15. Byval’tsev V. A., Kalinin A. A. Evaluation of the effectiveness of a decision support system in spinal neurosurgery for personalized use of minimally invasive technologies in the lumbar spine. Sovremennye tekhnologii v meditsine [Modern technologies in medicine], 2021, vol. 13, no. 5, pp. 13-23 (in Russian).
16. Wirries A., Geiger F., Hammad A., Oberkircher L., Blümcke I., Jabari S. Artificial intelligence facilitates decision making in the treatment of lumbar disc herniations. European Spine Journal, 2021, vol. 30, no. 8, pp. 2176-2184. https://doi.org/10.1007/s00586-020-06613-2
17. Campagner A., Berjano P., Lamartina C., Langella F., Lombardi G., Cabitza F. Assessment and prediction of spine surgery invasiveness with machine learning techniques. Computers in Biology and Medicine, 2020, vol. 121, art. 103796. https://doi.org/10.1016/j.compbiomed.2020.103796
18. Masalitina N. N., Kurochka K. S., Tsitko E. L. Mathematical model of decision making in the treatment of osteochondrosis of the lumbar spine. Informatika [Informatics], 2019, vol. 16, no. 1, pp. 24-37 (in Russian).
19. Rebrova O. Ju. Life cycle of a decision support system as medical technologies. Vrach i informatsionnye tekhnologii [Doctor and information technology], 2020, no. 1, pp. 27-37 (in Russian).
20. Kalinin A. A., Okoneshnikova A. K., Pestryakov Yu. Ya., Shepelev V. V., Byval’tsev V. A. Development of an algorithm for clinical and instrumental diagnosis of non-compressive lumbar pain syndromes to optimize the use of puncture surgical techniques. Innovatsionnaya meditsina Kubani [Innovative medicine of Kuban], 2020, no. 4, pp. 27-34 (in Russian).