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

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.