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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...

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Autores principales: 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
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
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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.
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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
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