Mathematical Model of Neural Network Development for Morphological Assessment of Repair and Remodeling of Bone Defect

Author:

Fedosova N. V.,Berchenko G. N.,Mashoshin D. V.

Publisher

Pleiades Publishing Ltd

Subject

Computational Mathematics,Modeling and Simulation

Reference23 articles.

1. R. Rojas, Neural Networks: A Systematic Introduction (Springer, Berlin, 1996).

2. S. Haykin, Neural Networks and Learning Machines, 3rd ed. (Prentice Hall, Upper Saddle River, NJ, 2009).

3. T. V. Brajlovskaya, G. N. Berchenko, N. V. Fedosova et al., “Method for diagnosing and correcting bone autograft after reconstructive osteoplastic surgery,” RF Patent No. 2 735 648 C1, Bull. No. 31 (2020).

4. L. Pantanowitz, G. M. Quiroga-Garza, L. Bien, R. Heled, D. Laifenfeld, C. Linhart et al., “An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study,” Lancet Digit Health 2 (8), e407-e416 (2020). https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30159-X. Cited May 12, 2021.

5. K. Nagpal and C. Mermel, “Using AI to identify the aggressiveness of prostate cancer,” Health, Official Google Blog (2020). https://blog.google/technology/health/using-ai-identify-aggressiveness-prostate-cancer/. Cited May 12, 2021.

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