Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

Abstract

Many environmental and genetic conditions may modify jaws growth. In orthodontics, the right treatment timing is crucial. This timing is a function of the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM is important. In orthodontics, the lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features to do it. Nowadays, Machine Learning (ML) and Artificial Intelligent (AI) tools are used for many medical and biological image processing, clustering and classification. This paper reports on the development of a Deep Learning (DL) method to determine directly from the images the degree of maturation of CVM classified in six degrees. Using 300 such images for training and 200 for evaluating and 100 for testing, we could obtain a 90% accuracy. The proposed model and method are validated by cross validation. The implemented software is ready for use by orthodontists.

Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs

“…Some studies reveal that CS 3 was the lowest in intra-rater absolute agreement (50% or less) compared to other CS [35]. A previous study on CVM classification using deep learning showed that CS 3 and 4 recorded relatively lower accuracy (72%) than other stages [24], although the accuracy differs in this study. The CS 3 stage, being a pubertal stage, contains a growth peak [5].…”

“…Currently, a fully automated system to predict skeletal age using deep learning on hand-wrist radiographs is widely used clinically, with high accuracy and visualization [19,20]. In contrast, CVM analysis studies on lateral cephalometric radiographs using deep learning differ in classification accuracy by about 80-90% due to differences in preprocessing techniques and deep learning models [21][22][23][24]. If CVM analysis is performed automatically on the lateral cephalometric radiograph, it can provide information on the skeletal maturity of growing children without specific training to clinicians and additional radiation exposure.…”

Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach

“…Seo et al, reported that in comparison to other stages, the C3 had the lowest AUC value 7 . According to another study, a deep learning-based architecture (SciKit-Learn and Keras with tensor ow backend) showed the lowest accuracy when classifying C3, and C4 17 . Kim et al, stated that the one stage error highly occurred in C3.…”

Application of Artificial Intelligence in Human Diagnosis and Public Health Management

“…Deep learning techniques can effectively accomplish the tasks of image detection, recognition and classi cation, so the introduction of deep learning techniques in the eld of imaging may help radiologists to complete various tasks of detection and diagnosis [19,20]. Pulmonary nodule detection using AI algorithm is an important part of AI medical eld [21]. Results show that the range of focus markers of the two detection methods is consistent, but the detection rate of AI software focus detection method based on deep learning model is signi cantly lower than that of manual detection method (P < 0.05), while the misdiagnosis rate and missed diagnosis rate are signi cantly higher than that of manual detection method (P < 0.05).…”