Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study

Abstract

Objective This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. Results The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography

“…This approach was already used in biomedical imaging to extend the size of the dataset. [ 20 21 ] Moreover, we added histogram equalization as a preprocessing step to enhance the image properties from various sources. Image contrast can be improved using histogram equalization in image preprocessing.…”

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages

“…The aim of the present study is to use a parallel structured deep learning (DL) method to develop a fully automated ML system to detect and classify the CVM stages. Recent research has focused on fully automated detection and categorization of the CVM stages using pre‐trained networks, with each study using a different data set 14,15 . In this study, we propose a novel DL model with parallel architecture to develop a fully automated system to detect and classify the CVM stages.…”

“…Recent research has focused on fully automated detection and categorization of the CVM stages using pre-trained networks, with each study using a different data set. 14,15 In this study, we propose a novel DL model with parallel architecture to develop a fully automated system to detect and classify the CVM stages. Our DL network has a parallel structure consisting of three sub-networks in each block which are independently trained with different initialization parameters.…”

Microcracks as tooth structural element: revealed by X-ray tomography and machine learning

“…Advances in digital dentistry are followed by increasing attempts to computerise certain routine clinical procedures, particularly the analysis of radiographs 23,24 . Artificial intelligence models for tooth and alveolar bone segmentation from cone-beam computed tomography images 23 , classification of cervical maturation degree and pubertal growth spurts from lateral cephalometric radiographs 24 would reduce the need for manual evaluation of radiographic images and contribute to treatment efficiency. However, to accurately visualise the tooth structure, a higher resolution 3D imaging technique is needed than has been used so far.…”