Cargando…
Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images
The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498199/ https://www.ncbi.nlm.nih.gov/pubmed/36140645 http://dx.doi.org/10.3390/diagnostics12092244 |
_version_ | 1784794698092642304 |
---|---|
author | Duman, Şuayip Burak Syed, Ali Z. Celik Ozen, Duygu Bayrakdar, İbrahim Şevki Salehi, Hassan S. Abdelkarim, Ahmed Celik, Özer Eser, Gözde Altun, Oğuzhan Orhan, Kaan |
author_facet | Duman, Şuayip Burak Syed, Ali Z. Celik Ozen, Duygu Bayrakdar, İbrahim Şevki Salehi, Hassan S. Abdelkarim, Ahmed Celik, Özer Eser, Gözde Altun, Oğuzhan Orhan, Kaan |
author_sort | Duman, Şuayip Burak |
collection | PubMed |
description | The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis. |
format | Online Article Text |
id | pubmed-9498199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94981992022-09-23 Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images Duman, Şuayip Burak Syed, Ali Z. Celik Ozen, Duygu Bayrakdar, İbrahim Şevki Salehi, Hassan S. Abdelkarim, Ahmed Celik, Özer Eser, Gözde Altun, Oğuzhan Orhan, Kaan Diagnostics (Basel) Article The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis. MDPI 2022-09-16 /pmc/articles/PMC9498199/ /pubmed/36140645 http://dx.doi.org/10.3390/diagnostics12092244 Text en © 2022 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 Duman, Şuayip Burak Syed, Ali Z. Celik Ozen, Duygu Bayrakdar, İbrahim Şevki Salehi, Hassan S. Abdelkarim, Ahmed Celik, Özer Eser, Gözde Altun, Oğuzhan Orhan, Kaan Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title | Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title_full | Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title_fullStr | Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title_full_unstemmed | Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title_short | Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images |
title_sort | convolutional neural network performance for sella turcica segmentation and classification using cbct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498199/ https://www.ncbi.nlm.nih.gov/pubmed/36140645 http://dx.doi.org/10.3390/diagnostics12092244 |
work_keys_str_mv | AT dumansuayipburak convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT syedaliz convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT celikozenduygu convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT bayrakdaribrahimsevki convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT salehihassans convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT abdelkarimahmed convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT celikozer convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT esergozde convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT altunoguzhan convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages AT orhankaan convolutionalneuralnetworkperformanceforsellaturcicasegmentationandclassificationusingcbctimages |