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Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early pos...

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Autores principales: Lyakhov, Pavel Alekseevich, Dolgalev, Alexander Alexandrovich, Lyakhova, Ulyana Alekseevna, Muraev, Alexandr Alexandrovich, Zolotayev, Kirill Evgenievich, Semerikov, Dmitry Yurievich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768332/
https://www.ncbi.nlm.nih.gov/pubmed/36567879
http://dx.doi.org/10.3389/fninf.2022.1067040
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author Lyakhov, Pavel Alekseevich
Dolgalev, Alexander Alexandrovich
Lyakhova, Ulyana Alekseevna
Muraev, Alexandr Alexandrovich
Zolotayev, Kirill Evgenievich
Semerikov, Dmitry Yurievich
author_facet Lyakhov, Pavel Alekseevich
Dolgalev, Alexander Alexandrovich
Lyakhova, Ulyana Alekseevna
Muraev, Alexandr Alexandrovich
Zolotayev, Kirill Evgenievich
Semerikov, Dmitry Yurievich
author_sort Lyakhov, Pavel Alekseevich
collection PubMed
description Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient’s condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.
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spelling pubmed-97683322022-12-22 Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants Lyakhov, Pavel Alekseevich Dolgalev, Alexander Alexandrovich Lyakhova, Ulyana Alekseevna Muraev, Alexandr Alexandrovich Zolotayev, Kirill Evgenievich Semerikov, Dmitry Yurievich Front Neuroinform Neuroscience Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient’s condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making. Frontiers Media S.A. 2022-12-07 /pmc/articles/PMC9768332/ /pubmed/36567879 http://dx.doi.org/10.3389/fninf.2022.1067040 Text en Copyright © 2022 Lyakhov, Dolgalev, Lyakhova, Muraev, Zolotayev and Semerikov. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lyakhov, Pavel Alekseevich
Dolgalev, Alexander Alexandrovich
Lyakhova, Ulyana Alekseevna
Muraev, Alexandr Alexandrovich
Zolotayev, Kirill Evgenievich
Semerikov, Dmitry Yurievich
Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title_full Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title_fullStr Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title_full_unstemmed Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title_short Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
title_sort neural network system for analyzing statistical factors of patients for predicting the survival of dental implants
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768332/
https://www.ncbi.nlm.nih.gov/pubmed/36567879
http://dx.doi.org/10.3389/fninf.2022.1067040
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