Authors
Akhmediev T.M.
Republican Specialized Scientific and Practical Medical Center of Neurosurgery, Tashkent, Republic of Uzbekistan
Abstract
According to the mathematical forecasting based on the methods of artificial neural networks, prognostic factors of the outcome of spinal dysraphia with hydrocephalus in 98 children are determined. The essence of the mathematical experiment was expressed in the task of various classifications on the sample and medical interpretation of the learning results of artificial neural networks. For numerical calculations, computer programs were used. Groups of diagnostic criteria have been established that affect the prognosis of treatment outcomes for spinal dysraphia with hydrocephalus in children. Comprehensive diagnostics and differentiated treatment, taking into account the established diagnostic criteria, reduced the number of adverse outcomes by 1.4 times.
Keywords: spinal dysraphia, hydrocephalus, artificial neural networks, outcome prediction.
References
1. Voronov VG. Klinika, diagnostika i khirurgicheskoe lechenie porokov razvitiya spinnogo mozga i pozvonochnika u detei. [dissertation] St. Petersburg; 2001. 215 p. (In Russ).
2. Gaidar BV. Gidrotsefaliya. Prakticheskaya neirokhirurgiya: Rukovodstvo dlya vrachei. St. Petersburg; 2002. pp. 552–570. (In Russ).
3. Ignatiev NA. An approximation for continuous functions using synthesis of neural networks with minimal configuration. Vychislitel’nye tekhnologii. 2009;14(1):80–84. (In Russ).
4. Ignatiev NA. Selecting an object’’s own space using nonlinear transformations of features. Informatsionnye tekhnologii. 2018;24(10):665–670. (In Russ).
5. Kovtun OP, Ovsova OV, Nikolaeva EB. Vrozhdennye poroki razvitiya tsentral’noi nervnoi sistemy u detei: otsenka faktorov riska i klinicheskie osobennosti. Neirokhirurgiya i nevrologiya detskogo vozrasta. 2007;(3):11–16. (In Russ).
6. Martynenko AA. Khirurgicheskoe lechenie detei so spinnomozgovymi gryzhami. [dissertation abstract] Omsk; 2010. 22 p. (In Russ).
7. Shamsiev AM, Aliyev B. Improving diagnosis and treatment of spinal hernias newborn. Uchenye zapiski Orlovskogo gosudarstvennogo universiteta. Seriya: Estestvennye, tekhnicheskie i meditsinskie nauki. 2014;2(7):108–109. (In Russ).
8. Alatas I, Canaz G, Kayran NA, et al. Shunt revision rates in myelomeningocele patients in the first year of life: a retrospective study of 52 patients. Childs Nerv Syst. 2018;34(5):919–923. doi: 10.1007/s00381-017-3663-z.
9. Colombo GL, Di Matteo S, Vinci M, et al. A cost-of-illness study of spina bifida in Italy. Clinicoecon Outcomes Res. 2013;5:309–316. doi: 10.2147/CEOR. S42841.
10. Ignat’ev NA, Rakhimov ShI. Synthesis of artificial neural networks with the minimal configuration and their transparency. In: Proceedings of the 3rd International Conference on Research and education in Mathematics; 2007 Apr 10-12; Kuala Lumpur, Malaysia. Malaysia; 2007. p. 98-101.
11. Kumar R, Singh SN. Spinal dysraphism: trends in nortern India. Pediatr Neurosurg. 2003;38(3):133–145. doi: 10.1159/000068819.
12. Norkett W, McLone DG, Bowman R. Current management strategies of hydrocephalus in the child with open spina bifida. Top Spinal Cord Inj Rehabil. 2016;22(4):241–246. doi: 10.1310/sci2204-241.
13. Tuli S, Drake J, Lamberti-Pasculli M. Long-term outcome of hydrocephalus management in myelomeningoceles. Childs Nerv Syst. 2003;19(5–6):286–291. doi: 10.1007/s00381-003-0759-4.
14. Zabsonre DS, Lankoande H, Zoungrana-Ouattara CF, et al. Association of Hydrocephalus with neural tube defect: our experience with the surgical treatment in one or in two operative stages (on separate days). Pediatr Neurosurg. 2019;54(1): 1–5. doi: 10.1159/000494562.
