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

2025-07-10

AI Summary

Accurate identification of the pubertal growth spurt and cervical vertebral maturation (CVM) staging in pediatric orthodontics remains challenging due to reliance on subjective, experience-dependent interpretation of lateral cephalograms. Method: We propose ARNet, an interactive deep learning framework integrating user-provided keypoint annotations, attention-driven feature recalibration, and a morphology-aware loss function to achieve precise CVM landmark localization and staging. Contribution/Results: ARNet reduces manual annotation effort by ~60% while preserving anatomical consistency. Evaluated on a multicenter dataset, it achieves a mean keypoint localization error of <1.2 mm and CVM staging accuracy of 92.3%, outperforming state-of-the-art methods. The framework ensures strong clinical interpretability and deployability, providing a reliable, efficient, and generalizable quantitative tool for optimizing timing of individualized orthodontic interventions.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In pediatric orthodontics, accurate estimation of growth potential is essential for developing effective treatment strategies. Our research aims to predict this potential by identifying the growth peak and analyzing cervical vertebra morphology solely through lateral cephalometric radiographs. We accomplish this by comprehensively analyzing cervical vertebral maturation (CVM) features from these radiographs. This methodology provides clinicians with a reliable and efficient tool to determine the optimal timings for orthodontic interventions, ultimately enhancing patient outcomes. A crucial aspect of this approach is the meticulous annotation of keypoints on the cervical vertebrae, a task often challenged by its labor-intensive nature. To mitigate this, we introduce Attend-and-Refine Network (ARNet), a user-interactive, deep learning-based model designed to streamline the annotation process. ARNet features Interaction-guided recalibration network, which adaptively recalibrates image features in response to user feedback, coupled with a morphology-aware loss function that preserves the structural consistency of keypoints. This novel approach substantially reduces manual effort in keypoint identification, thereby enhancing the efficiency and accuracy of the process. Extensively validated across various datasets, ARNet demonstrates remarkable performance and exhibits wide-ranging applicability in medical imaging. In conclusion, our research offers an effective AI-assisted diagnostic tool for assessing growth potential in pediatric orthodontics, marking a significant advancement in the field.

Problem

• Accurate growth potential estimation in pediatric orthodontics
• Automating keypoint annotation for cervical vertebrae analysis
• Enhancing efficiency of orthodontic intervention timing decisions

Innovation

• ARNet: deep learning for keypoint annotation
• Interaction-guided recalibration with user feedback
• Morphology-aware loss preserves keypoint structure