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 o

SUBMITTER: Jiang F 

PROVIDER: S-EPMC11938625 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature