Cargando…
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...
Autores principales: | , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1785063285362524160 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10294869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenyichieh improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT chenmingyi improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT chentsungyi improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT chanmeiling improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT huangyayun improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT liuyulin improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT leepeiting improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT linguanjhih improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT litaifeng improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT chenchiungan improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT chenshihlun improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT likuochen improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm AT abupatriciaangelar improvingdentalimplantoutcomescnnbasedsystemaccuratelymeasuresdegreeofperiimplantitisdamageonperiapicalfilm |