AI-Driven Cervical Vertebra Bone Age Assessment
Discover Beauty
September 16, 2024
Craniofacial growth is an integral part of orthodontic diagnosis and treatment planning. Growth is characterized by variation in the amount, rate, time, pattern, and progress towards maturity. The developmental status of a growing child can be assessed by various indicators: Chronological Age, Dental Development, Secondary Sexual characteristics, Peak Height Velocity, Skeletal Maturation.
_ PROBLEM AREAS
Challenges in Cervical Vertebral Bone Age Assessment
Additional Radiograph, Radiation and Cost Efficiency
The cervical vertebral bone age assessment requires an extra radiograph, increasing both radiation exposure and diagnostic costs. While the cervical vertebral maturation method reduces radiation exposure and diagnostic costs, it still necessitates an additional radiographic procedure.Subjective Assessment Methods and Interoperator Inconsistency
Traditional methods rely on visual comparison with a standard atlas, making them prone to operator subjectivity and potential errors in determining bone age. Differences in interpretation between operators can introduce variability, which affects the consistency and accuracy of skeletal age determination.Cultural and Demographic Variability
Existing regression formulas are influenced by gender and racial origin, leading to concerns about their applicability in diverse populations, such as those in Maharashtra, where literature is scarce._ SOLUTION
AI-Powered Cervical Vertebrae Age Calculation Tool
An advanced AI/ML tool has been developed to automatically identify and mark key landmarks on the C3 and C4 cervical vertebrae from X-ray images. This tool enables accurate linear measurements, which are then used to calculate skeletal age through a regression formula. The AI model was trained on annotated lateral cephalogram images and fine-tuned using a robust dataset to ensure precise landmark detection, even in images with varying quality and orientations.
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Radiographs Dataset
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Accuracy
_ RESULTS
Enhancing Precision in Cervical Vertebrae Age Calculation
This AI-powered solution significantly improves the accuracy and efficiency of identifying key landmarks on the C3 and C4 cervical vertebrae from X-ray images. By integrating annotated datasets, advanced AI algorithms, and regression-based analysis, the tool ensures precise skeletal age calculation. This innovation holds great promise for enhancing medical imaging and diagnostics, offering a more reliable, automated alternative to traditional methods in clinical settings.