scholarly journals Convolutional Neural Networks For Apical Lesion Segmentation From Panoramic Radiographs

2021 ◽  
Author(s):  
Il-Seok Song ◽  
Hak-Kyun Shin ◽  
Ju-Hee Kang ◽  
Jo-Eun Kim ◽  
Kyung-Hoe Huh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Early Age ◽  
Next Generation ◽  
Lesion Segmentation ◽  
Panoramic Radiographs ◽  
Diagnostic Methodology ◽  
Apical Lesions

Abstract Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising next-generation artificial intelligence (AI) in the field of medical and dental researches, which can further provide an effective diagnostic methodology allowing for detection of diseases at early age. This study was, thus, aimed to evaluate performances for apical lesion segmentation from panoramic radiographs using two CNN algorithms including U-Net and FPN. A total of 1000 panoramic radiographs showing apical lesions were separated into training (n = 800, 80%), validation (n = 100, 10%), and test (n = 100, 10%) dataset, respectively. These datasets were further incorporated to construct CNN models using two algorithms, respectively. The performances of identifying apical lesions were evaluated after calculating precision, recall, and F1-score from both CNN models. Both U-Net and FPN algorithms provided considerably good performances in identifying apical lesions in panoramic radiographs.

scholarly journals A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Ibrahim S. Bayrakdar ◽  
Kaan Orhan ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Hande Sağlam ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Maxillofacial Radiology ◽  
Lesion Segmentation ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Periapical Lesions ◽  
Oral And Maxillofacial Radiology ◽  
Apical Lesions ◽  
Clinical Problems

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

scholarly journals A primer on deep learning and convolutional neural networks for clinicians

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lara Lloret Iglesias ◽  
Pablo Sanz Bellón ◽  
Amaia Pérez del Barrio ◽  
Pablo Menéndez Fernández-Miranda ◽  
David Rodríguez González ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Capabilities ◽  
Computer Vision Applications ◽  
Fully Connected ◽  
Imaging Perspective ◽  
Adaptive Momentum

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

Train Deep Learning Models using subsurface geological images datasets

2021 ◽  
Author(s):  
Ramy Abdallah ◽  
Clare E. Bond ◽  
Robert W.H. Butler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Models ◽  
Structural Interpretation ◽  
Performance Accuracy ◽  
Learning Techniques ◽  
Machine Learning Models

<p>Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model’s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.</p>

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

2019 ◽  
Vol 134 (1) ◽  
pp. 52-55 ◽  
Author(s):  
J Huang ◽  
A-R Habib ◽  
D Mendis ◽  
J Chong ◽  
M Smith ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Sinus Surgery ◽  
Computed Tomography Scans ◽  
Ethmoidal Artery ◽  
Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

scholarly journals Tooth detection and numbering in panoramic radiographs using convolutional neural networks

2019 ◽  
Vol 48 (4) ◽  
pp. 20180051 ◽  
Author(s):  
Dmitry V. Tuzoff ◽  
Lyudmila N. Tuzova ◽  
Michael M. Bornstein ◽  
Alexey S. Krasnov ◽  
Max A. Kharchenko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Panoramic Radiographs

Artificial intelligence‐based detection of pharyngeal cancer using convolutional neural networks

2020 ◽  
Vol 32 (7) ◽  
pp. 1057-1065 ◽  
Author(s):  
Atsuko Tamashiro ◽  
Toshiyuki Yoshio ◽  
Akiyoshi Ishiyama ◽  
Tomohiro Tsuchida ◽  
Kazunori Hijikata ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pharyngeal Cancer
Sign in / Sign up

Export Citation Format

Share Document