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Related Experiment Video

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Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age

Jinhee Kim1, Taesung Kim1, Taewoo Kim1

  • 1Kim Jaechul Graduate School of Artificial Intelligence, KAIST, South Korea.

Medical Image Analysis
|August 6, 2025
PubMed
Summary

This study introduces an AI tool, Attend-and-Refine Network (ARNet), to efficiently predict pediatric orthodontic growth potential using cervical vertebra maturation (CVM) analysis from radiographs, improving treatment timing.

Keywords:
Cervical vertebral maturationGrowth potential estimationInteractive keypoint estimationRadiograph analysis

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Area of Science:

  • Orthodontics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate estimation of growth potential is crucial for effective pediatric orthodontic treatment.
  • Cervical vertebral maturation (CVM) analysis from lateral cephalometric radiographs is a key method.
  • Manual keypoint annotation for CVM analysis is labor-intensive and time-consuming.

Purpose of the Study:

  • To develop an AI-assisted tool for predicting growth potential in pediatric orthodontics.
  • To streamline the annotation of cervical vertebrae keypoints for CVM analysis.
  • To enhance the efficiency and accuracy of determining optimal orthodontic intervention timing.

Main Methods:

  • Utilized lateral cephalometric radiographs for CVM analysis.
  • Introduced Attend-and-Refine Network (ARNet), a user-interactive deep learning model.
  • Implemented an Interaction-guided recalibration network and a morphology-aware loss function within ARNet.

Main Results:

  • ARNet significantly reduces manual effort in keypoint identification.
  • The model demonstrates enhanced efficiency and accuracy in CVM analysis.
  • Extensive validation across datasets confirms ARNet's remarkable performance and applicability.

Conclusions:

  • ARNet offers an effective AI-assisted diagnostic tool for assessing pediatric orthodontic growth potential.
  • The developed methodology improves the reliability and efficiency of determining orthodontic treatment timing.
  • This research represents a significant advancement in AI applications for orthodontic diagnostics.