Lateral Cephalogram AI Segmentation for Cervical Vertebral Maturation Staging
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Solution Overview
Problem
Existing methods for assessing cervical vertebral maturation (CVM) in adolescents are limited by the need for additional radiographic tests, which cause hesitation among orthodontists and patients, and there is a scarcity of data for adult orthodontic treatments, necessitating a more efficient and accurate method for predicting CVM stages using lateral cephalograms.
Innovation Solution
A computer-aided classification system that processes lateral cephalograms to extract cervical vertebrae contours and markers, employing a multi-stage machine learning classification process to accurately predict CVM stages by generating iso-contours and using a customized U-Net architecture for segmentation, along with a multi-level classification approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-wrist radiographs are used to evaluate CVM stages, then measurement precision is improved, but device complexity and loss of time increase due to additional radiographic tests
Solution Approach 1:
The patent extracts and utilizes existing lateral cephalogram images that are already part of standard orthodontic evaluation, removing the need for additional hand-wrist radiographs. The system extracts cervical vertebrae contours and markers from these existing images to determine CVM stages, thereby maintaining measurement precision while reducing the complexity of required radiographic tests.
Solution Approach 2:
The lateral cephalogram, which is already universally used for orthodontic analysis, is extended to serve dual purposes: both for orthodontic treatment planning and for CVM stage evaluation. This multi-functionality eliminates the need for separate hand-wrist radiographs, addressing the contradiction between measurement precision and device complexity.
2Ease of operation
If lateral cephalograms are used for CVM assessment, then ease of operation is improved, but measurement precision may be compromised due to limited data availability
Solution Approach 1:
The patent replaces manual visual assessment methods with an automated machine learning-based system. The system uses deep learning models to extract cervical vertebrae contours, identify maturation markers, and predict CVM stages from lateral cephalograms, thereby maintaining ease of operation while significantly improving measurement precision through computational analysis.
Solution Approach 2:
The patent introduces an intermediary computational system that processes lateral cephalogram images through multiple processing stages including bone age estimation, cervical vertebrae segmentation, and marker identification. This intermediary system bridges the gap between the simplicity of using existing cephalograms and the need for high measurement precision, enabling accurate CVM assessment without compromising either aspect.
3Productivity
If automated AI-based analysis is implemented, then productivity is improved, but device complexity increases due to sophisticated processing requirements
Solution Approach 1:
The patent segments the complex AI-based analysis process into distinct functional modules: bone age estimation module, cervical vertebrae segmentation module, marker identification module, and CVM stage prediction module. Each module handles a specific aspect of the analysis, thereby improving productivity through automated processing while managing device complexity through modular architecture that can be implemented incrementally.
Data Source
AI summary
Computer-aided classification systems and methods for prediction of cervical vertebral maturation stages include extracting cervical vertebrae from medical images, parcellating the cervical vertebrae to generate a plurality of iso-contours for each segmented cervical vertebrae, extracting local and global imaging markers that describe the shape and appearance of each extracted cervical vertebrae, and classifying, using a two-stage machine learning classifier, the cervical vertebral maturation stage of the extracted cervical vertebrae.