Deep learning based quantitative cervical vertebral maturation analysis.

Abstract

Objectives: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis.

Methods: This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. The CVnet system was designed as a two-step method with a comprehensive evaluation of various regions of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify precision maturation stages. The accuracy of landmark localization was assessed by success detection rate and student t-test. The QCVM diagnostic accuracy test was conducted to evaluate the assistant performances of our system for six junior orthodontists.

Results: Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. Additionally, the identification accuracy of QCVM stages was 69.52%, resulting in an enhancement of 10.95% in the staging accuracy of junior orthodontists in the diagnostic test.

Conclusions: This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning.