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Presnyakov E.V.

Petrovsky National Research Center of Surgery;
JSC "Histograft"

Kurbonov Kh.R.

Samarkand State Medical University

Zhemkov N.I.

Petrov National Research Center of Oncology

Podluzhny P.S.

Petrov National Research Center of Oncology

Dalgatov M.G.

Petrovsky National Research Center of Surgery

Melchenko D.S.

Petrovsky National Research Center of Surgery

Bozo I.Ya.

Petrovsky National Research Center of Surgery;
JSC "Histograft"

Deev R.V.

Petrovsky National Research Center of Surgery

Artificial intelligence-based software for digital assessment of reparative bone tissue regeneration

Authors:

Presnyakov E.V., Kurbonov Kh.R., Zhemkov N.I., Podluzhny P.S., Dalgatov M.G., Melchenko D.S., Bozo I.Ya., Deev R.V.

More about the authors

Journal: Regenerative Biotechnologies, Preventive, Digital and Predictive Medicine. 2025;2(1): 19‑24

DOI: 10.17116/rbpdpm2025201119

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Table of contents

ARTIFICIAL INTELLIGENCE-BASED SOFTWARE FOR DIGITAL ASSESSMENT OF REPARATIVE BONE TISSUE REGENERATION
ARTIFICIAL INTELLIGENCE-BASED SOFTWARE FOR DIGITAL ASSESSMENT OF REPARATIVE BONE TISSUE REGENERATION

To cite this article:

Presnyakov EV, Kurbonov KhR, Zhemkov NI, et al. . Artificial intelligence-based software for digital assessment of reparative bone tissue regeneration. Regenerative Biotechnologies, Preventive, Digital and Predictive Medicine. 2025;2(1):19‑24. (In Russ.)
https://doi.org/10.17116/rbpdpm2025201119

 
Подписка 2025-2 (Regenerative Biotechnologies, Preventive, Digital and Predictive Medicine)
 
МСФ на ЯМ
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OBJECTIVE

To develop artificial intelligence-based software algorithm for quantitative assessment of reparative bone tissue regeneration, as well as to compare the results of histomorphometric analysis carried out by neural network and human.

MATERIAL AND METHODS

Morphometric parameters were determined and standardized. The authors developed artificial intelligence-based software algorithm with subsequent neural network training based on high-resolution images. Efficacy of human and software algorithm was compared.

RESULTS

Original software allows for automatic analysis of bone regenerate tissue composition within the shortest possible time.

CONCLUSION

Original software may be equivalent for quick and effective analysis of bone regenerate tissue components. These data may be comparable with manual marking regarding reproducibility and accuracy.

Keywords:

bone
regeneration
bone grafting
artificial intelligence

Authors:

Presnyakov E.V.

Petrovsky National Research Center of Surgery;
JSC "Histograft"

Kurbonov Kh.R.

Samarkand State Medical University

Zhemkov N.I.

Petrov National Research Center of Oncology

Podluzhny P.S.

Petrov National Research Center of Oncology

Dalgatov M.G.

Petrovsky National Research Center of Surgery

Melchenko D.S.

Petrovsky National Research Center of Surgery

Bozo I.Ya.

Petrovsky National Research Center of Surgery;
JSC "Histograft"

Deev R.V.

Petrovsky National Research Center of Surgery

Received:

23.11.2024

Accepted:

15.01.2025

List of references:

  1. Nikonenko AG. Vvedenie v kolichestvennuyu gistologiyu. Kiev: Kniga-plyus; 2013. (In Russ.).
  2. Power B. Artificial Intelligence Is Almost Ready for Business. Accessed January 29,2025. https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business
  3. Faustova KI. Neural networks: application today and development prospects. Territoriya nauki. 2017;4:83-87. (In Russ.).
  4. Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017;39(4):640-651.  https://doi.org/10.1109/TPAMI.2016.2572683
  5. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(4):834-848.  https://doi.org/10.1109/TPAMI.2017.2699184
  6. Chen L, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. European Conference on Computer Vision. 2018:833-851.  https://doi.org/10.48550/arXiv.1802.02611
  7. Sankar A. A Primer on Atrous (Dilated) and Depth-wise Separable Convolutions. Accessed January 29,2025. https://towardsdatascience.com/a-primer-on-atrous-convolutions-and-depth-wise-separable-convolutions-443b106919f5
  8. Volkov AV, Bolshakova GB. Histomorphometry of bone tissue in regenerative medicine. Klinicheskaya i e`ksperimental`naya morfologiya. 2013;3(7):65-72. (In Russ.).
  9. Kotenko KV, Eremin II, Moroz BB, Bushmanov AYu, Nadezhina NM, Galstyan IA, Grinakovskaya OS, Aksenenko AV, Deshevoy YuB, Lebedev VG, Slobodina TS, Zhgutov YuA, Lauk-Dubitsky SE, Eremin PS. Cell technologies in the treatment of radiation burns: experience Burnazyan Federal Medical Biophysical Center. Kletochnaya transplantologiya i tkanevaya inzheneriya. 2012;7(2):97-102. (In Russ.).
  10. Kotenko KV, Moroz BB, Nasonova TA, Dobrynina OA, Lipengolts AA, Gimadova TI, Deshevoy YuB, Lebedev VG, Lyrshchikova AV, Eremin II. Experimental model of severe local radiation injuries of the skin after X-rays. Patologicheskaya fiziologiya i e`ksperimental`naya terapiya. 2013;57(4):121-123. (In Russ.).
  11. Kotenko KV, Moroz BB, Nadezhina NM, Galstyan IA, Onishchenko NA, Eremin II, Deshevoy YuB, Lebedev VG, Slobodina TS, Dubitsky SE, Grinakovskaya OS, Zhgutov YuA, Bushmanov AY. Mesenchymal stem cell transplantation cells in the treatment of radiation damage to the skin. Patologicheskaya fiziologiya i e`ksperimental`naya terapiya. 2011;1:20-25. (In Russ.).
  12. Presnyakov EV, Komarov AV, Bozo IYa, Deev RV. Biotechnological method for bone tissue recovery: theoretical basis and clinical application. Regenerative Biotechnologies, Preventive, Digital and Predictive Medicine. 2024;1(2):12-17. (In Russ.). https://doi.org/10.17116/rbpdpm2024102112

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