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Prediction of Tensile Strength of Biotissue Laser Welds by Machine Learning Methods

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Biomedical Engineering Aims and scope

The number of cases needing restoration of the integrity of patients' biological tissues increases year on year. Despite the generally successful application of existing methods for restoring the integrity of biological tissues, it is important to create new methods producing joints which are hermetically sealed in relation to liquids and have a uniform distribution of mechanical stresses. A new potential method is laser welding of biological tissues, though effective application of this method requires the process of restoring the integrity of biological tissue to be optimized with respect to increasing the tensile strength of joints. The key step in optimization is determination of the tensile strength in relation to a number of laser welding parameters. This study proposes optimization using tensile strength values predicted by machine learning methods in place of experimentally acquired data. Review of scientific reports produced a dataset of 394 objects. Models based on an ensemble of decision trees showed the highest prediction accuracy.

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Authors and Affiliations

  1. Institute of Biomedical Systems, National Research University of Electronic Technology, Zelenograd, Moscow, Russia

    D. I. Ryabkin, V. V. Suchkova & A. Yu. Gerasimenko

  2. Institute of Bionic Technologies and Engineering, I. M. Sechenov First Moscow State Medical University, Russian Ministry of Health (Sechenov University), Moscow, Russia

    D. I. Ryabkin & A. Yu. Gerasimenko

Authors
  1. D. I. Ryabkin
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  2. V. V. Suchkova
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  3. A. Yu. Gerasimenko
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Correspondence to D. I. Ryabkin.

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Translated from Meditsinskaya Tekhnika, Vol. 57, No. 2, March–April, 2023, pp. 26–29.

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Ryabkin, D.I., Suchkova, V.V. & Gerasimenko, A.Y. Prediction of Tensile Strength of Biotissue Laser Welds by Machine Learning Methods. Biomed Eng 57, 112–115 (2023). https://doi.org/10.1007/s10527-023-10280-0

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  • DOI: https://doi.org/10.1007/s10527-023-10280-0

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