Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.
Key Details
- 1ARNet-v2 uses lateral cephalometric radiographs to identify cervical vertebrae keypoints.
- 2The model allows a single clinician correction to propagate, boosting efficiency and accuracy.
- 3Tested on 5,700+ radiographs and across four public datasets, ARNet-v2 reduced prediction failures by up to 67%.
- 4Manual annotation requirements are halved compared to conventional approaches.
- 5The AI may reduce the need for additional hand–wrist X-rays, lowering radiation exposure for pediatric patients.
- 6Published in Medical Image Analysis, July 2025.
Why It Matters
This model offers significant efficiency and diagnostic accuracy improvements for pediatric orthodontics, potentially lowering cost and radiation exposure for young patients. Its success could pave the way for further AI integration in radiology workflows and broader medical imaging challenges.
Source
EurekAlert
Related News
•EurekAlert
Dana-Farber Showcases AI and Clinical Trial Advances at ESMO 2025
Dana-Farber researchers present major cancer clinical trial results, including AI-driven data analysis, at ESMO Congress 2025.
•EurekAlert
Einstein College Awarded $18M NIH Grant to Develop AI Tools for Mental Health Crisis Prediction
Albert Einstein College of Medicine received an $18 million NIH grant to create AI-based tools for predicting mental health crises using cognitive monitoring.
•EurekAlert
Imaging Reveals Skull Changes and Immune Impact in Glioblastoma
Advanced imaging uncovers that glioblastoma affects the skull and immune system, not just the brain.