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Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stabilit...

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Autores principales: Chen, Yi-Chieh, Chen, Ming-Yi, Chen, Tsung-Yi, Chan, Mei-Ling, Huang, Ya-Yun, Liu, Yu-Lin, Lee, Pei-Ting, Lin, Guan-Jhih, Li, Tai-Feng, Chen, Chiung-An, Chen, Shih-Lun, Li, Kuo-Chen, Abu, Patricia Angela R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294869/
https://www.ncbi.nlm.nih.gov/pubmed/37370571
http://dx.doi.org/10.3390/bioengineering10060640
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author Chen, Yi-Chieh
Chen, Ming-Yi
Chen, Tsung-Yi
Chan, Mei-Ling
Huang, Ya-Yun
Liu, Yu-Lin
Lee, Pei-Ting
Lin, Guan-Jhih
Li, Tai-Feng
Chen, Chiung-An
Chen, Shih-Lun
Li, Kuo-Chen
Abu, Patricia Angela R.
author_facet Chen, Yi-Chieh
Chen, Ming-Yi
Chen, Tsung-Yi
Chan, Mei-Ling
Huang, Ya-Yun
Liu, Yu-Lin
Lee, Pei-Ting
Lin, Guan-Jhih
Li, Tai-Feng
Chen, Chiung-An
Chen, Shih-Lun
Li, Kuo-Chen
Abu, Patricia Angela R.
author_sort Chen, Yi-Chieh
collection PubMed
description As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.
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spelling pubmed-102948692023-06-28 Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film Chen, Yi-Chieh Chen, Ming-Yi Chen, Tsung-Yi Chan, Mei-Ling Huang, Ya-Yun Liu, Yu-Lin Lee, Pei-Ting Lin, Guan-Jhih Li, Tai-Feng Chen, Chiung-An Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Bioengineering (Basel) Article As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes. MDPI 2023-05-25 /pmc/articles/PMC10294869/ /pubmed/37370571 http://dx.doi.org/10.3390/bioengineering10060640 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Yi-Chieh
Chen, Ming-Yi
Chen, Tsung-Yi
Chan, Mei-Ling
Huang, Ya-Yun
Liu, Yu-Lin
Lee, Pei-Ting
Lin, Guan-Jhih
Li, Tai-Feng
Chen, Chiung-An
Chen, Shih-Lun
Li, Kuo-Chen
Abu, Patricia Angela R.
Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title_full Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title_fullStr Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title_full_unstemmed Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title_short Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film
title_sort improving dental implant outcomes: cnn-based system accurately measures degree of peri-implantitis damage on periapical film
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10294869/
https://www.ncbi.nlm.nih.gov/pubmed/37370571
http://dx.doi.org/10.3390/bioengineering10060640
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