(Press-News.org)
Orthodontic treatment is most effective when timed to coincide with a child’s growth peak. Traditionally, clinicians estimate growth by examining X-ray images of the cervical vertebrae—the neck bones visible in routine dental radiographs. However, this process requires careful manual annotation of specific points on the bones, a task that is both time-consuming and prone to variation between observers.
In a new article, researchers from Korea University Anam Hospital, KAIST, and the University of Ulsan introduced an artificial intelligence (AI) system designed to overcome these challenges. The paper was made available online on 29 July 2025 and published in Medical Image Analysis, Volume 106, Issue December 2025. The work, led by Dr. Jinhee Kim and Professor In-Seok Song, presents the Attend-and-Refine Network (ARNet-v2), an interactive deep learning model that streamlines growth assessment from a single lateral cephalometric radiograph.
ARNet-v2 automatically identifies skeletal landmarks on cervical vertebrae, allowing clinicians to predict a child’s pubertal growth peak. Unlike conventional techniques, the model requires minimal input: a single manual correction can be propagated across related anatomical points in the image, significantly improving both efficiency and accuracy. Dr. Kim explained, “Importantly, the model allows a single correction by a clinician to automatically propagate to related keypoints across the image, enabling state-of-the-art accuracy with far fewer user interactions.”
The model was trained and tested on more than 5,700 radiographs and validated across four public medical imaging datasets. In direct comparisons, ARNet-v2 outperformed existing systems, reducing prediction failures by up to 67% and halving the number of manual adjustments needed. This interactive approach not only enhances precision but also lowers the overall cost of medical image annotation.
Clinically, the system offers immediate benefits. By extracting detailed cervical vertebra features from one radiograph, ARNet-v2 can replace additional hand–wrist X-rays, reducing radiation exposure for children while ensuring timely orthodontic decision-making. “Clinically, the model’s ability to extract precise cervical-vertebra keypoints from a single X-ray enables accurate estimation of a child’s pubertal growth peak, a key factor in determining the timing of orthodontic treatment. By replacing traditional hand-wrist radiographs, it can lower radiation exposure and costs for young patients,” noted Prof. Song.
Beyond orthodontics, the Attend-and-Refine framework shows promise for broader medical imaging challenges, such as brain MRI, retinal scans, and cardiac ultrasound. It may even extend to non-medical domains like robotics and autonomous driving, where rapid, high-quality annotation is crucial.
For clinical workflows, ARNet-v2 provides a notable boost in efficiency, easing workloads in busy hospitals and supporting resource-limited clinics or remote consultations. Looking ahead, AI-assisted bone-age and growth assessment could become a routine component of paediatric care, combining automated analysis with personalised treatment planning. As Dr. Kim emphasized, “Together, these aspects position our work as a significant step forward in AI-assisted bone-age assessment and pediatric orthodontics.”
By reducing unnecessary imaging, lowering costs, and improving diagnostic accuracy, this system offers clear advantages for both clinicians and young patients.
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Reference
DOI: 10.1016/j.media.2025.103715
About Korea University College of Medicine
Korea University College of Medicine is the medical school of Korea University. It is located in Seoul, South Korea. As one of the oldest medical schools in South Korea, it has been historically regarded as one of the country's top medical schools. The school was founded as Chosun Women's Medical Training Institute in 1928 by Rosetta Sherwood Hall. The institute was subsequently renamed several times and ultimately merged with Korea University to become Korea University College of Medicine. So far, the school has produced over 7,000 graduates, most of whom are working as prominent physicians and public health advocates worldwide.
Website: https://medicine.korea.ac.kr/en/index.do
About Prof. In-Seok Song
Prof. In-Seok Song is a Professor of Oral and Maxillofacial Surgery at Korea University Anam Hospital. His research focuses on the diagnosis and surgical treatment of oral and maxillofacial diseases, as well as the development of AI-based medical imaging, dental SaMD, and digital twin simulation technologies. He is also engaged in medical robotics and AI-driven diagnostic and therapeutic algorithms. Before becoming a full professor at Korea University, he served as a visiting scholar at George Washington University and Children’s National Hospital (2023–2024). Prof. Song received his DDS, MS, and PhD in Oral and Maxillofacial Surgery from Seoul National University.
About Dr.Jinhee Kim
Dr. Jinhee Kim is a Staff AI Engineer at Samsung Research. Her research focuses on large multimodal models (LMMs), medical image analysis, and structured data analysis such as tabular and time-series data. Her work has been published in top-tier venues such as NeurIPS, ECCV, ICLR, MICCAI, and Medical Image Analysis. She recently completed her Ph.D. in Artificial Intelligence at KAIST under the supervision of Professor Jaegul Choo in August 2024. She earned her B.S. in Computer Science and Engineering from Korea University, graduating summa cum laude in February 2019.
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