A Novel Continuous Classification System for the Cervical Vertebrae Maturation (CVM) Stages Using Convolutional Neural Networks

Abstract

Introduction: We aim to apply deep learning methods to achieve the continuous classification of the Cervical Vertebrae Maturation (CVM) stages and to assess skeletal maturity. We propose a novel two-stage system with a parallel structure network and a sigmoid-based method to generate the continuous-valued cervical vertebrae maturity (CVCVM) parameter. Methods: A total of 1398 Cephalometric radiographs are meticulously annotated and stratified based on their respective Cervical Vertebrae Maturation (CVM) stages, with 1018 images allocated for training and validation, also the remaining 380 collected from 25 patients and labeled by two clinicians for testing. The images are further partitioned according to gender. A two-stage system is devised for the continuous estimation of CVM stages. A parallel-structure neural network called TriPodNet is trained to gauge the likelihood of each class for the maturation stage in the first part of the proposed system. The network is supplied with two different types of input, namely a radiographic X-ray image and chronological age. Probability values of individual classes are generated and mapped onto a continuous stage by two different methods, namely weighted averaging and sigmoid-based regression. The correlation of the estimated Cervical Vertebrae Maturation parameter is assessed using the Pearson correlation coefficient. In order to ascertain the validity of TriPodNet, the Permutation Importancemethod is employed to gauge the impact of each input. Results: TriPodNet is able to achieve a validation accuracy of 81.17% for female subjects and 75.96% for male subjects. During testing, the class probability values of the inputs are determined by TriPodNet, and the continuous estimation parameters are obtained by applying two distinct mapping functions. The sigmoid-based regression method produces an average correlation coefficient value of 0.910 with the first clinician and 0.944 with the second clinician for male patients, while for female patients the values were 0.910 and 0.918 respectively. Conversely, the weighted average method performs less effectively, with average correlation coefficient values of 0.913 and 0.904 for male patients with the first and second clinicians respectively. For female patients, the method produces similar results with an average correlation coefficient value of 0.901 and 0.896 with the first and second clinicians respectively. The Permutation Importance method shows that the image input and the chronological age input collaboratively contribute to the model in producing the accurate output. Conclusion: The proposed method to determine the CVM stages in a continuous stages pattern CVCVM using Convolutional Neural Network (CNN) achieved novel high correlation results compared to true labels and it is more consistent with the gradual growth changes. It is observed to be a unique way to represent skeletal maturity and assess growth.