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Background <p>Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires specialized expertise. In this study, we propose Skel-Net, a novel anatomic prior-guided deep learning network designed to estimate ANB angle changes over five years in children and adolescents aged 8–16.</p> Methods <p>In a two-stage approach, Skel-Net combines cephalometric landmark detection via Ceph-Net and multichannel inputs, including two-dimensional heatmaps and ANB priors, to enhance prediction accuracy. A dataset of 612 lateral cephalograms from 245 patients was used to train and validate the model, and its performance was compared against DenseNet121, MobileNetV2, ResNet101, and VGG16.</p> Results <p>Skel-Net outperformed the other models with the lowest prediction errors (mean absolute error: 1.021 degrees; root mean squared error: 1.338 degrees) and the highest <i>R</i><sup>2</sup> value (0.517), demonstrating robust predictive capabilities.</p> Conclusions <p>By leveraging anatomic priors and longitudinal data, Skel-Net enables dynamic and personalized predictions of craniofacial growth. This framework will facilitate early and precise orthodontic interventions, enhancing treatment efficiency, stability, and overall patient outcomes.</p>

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Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月31日

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  • Eun Sun Song,
  • Su Yang,
  • Won-Jin Yi,
  • Seung-Pyo Lee

摘要

Background

Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires specialized expertise. In this study, we propose Skel-Net, a novel anatomic prior-guided deep learning network designed to estimate ANB angle changes over five years in children and adolescents aged 8–16.

Methods

In a two-stage approach, Skel-Net combines cephalometric landmark detection via Ceph-Net and multichannel inputs, including two-dimensional heatmaps and ANB priors, to enhance prediction accuracy. A dataset of 612 lateral cephalograms from 245 patients was used to train and validate the model, and its performance was compared against DenseNet121, MobileNetV2, ResNet101, and VGG16.

Results

Skel-Net outperformed the other models with the lowest prediction errors (mean absolute error: 1.021 degrees; root mean squared error: 1.338 degrees) and the highest R2 value (0.517), demonstrating robust predictive capabilities.

Conclusions

By leveraging anatomic priors and longitudinal data, Skel-Net enables dynamic and personalized predictions of craniofacial growth. This framework will facilitate early and precise orthodontic interventions, enhancing treatment efficiency, stability, and overall patient outcomes.