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Related Experiment Video

Updated: Apr 12, 2026

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Deep-Learning-Based Automatic Measurement of the Distance Between the Maxillary Sinus and Maxillary Posterior Teeth

Cheng-Ye Li1,2,3, Ming-Ming Zhang1,2,3, Ke-Xin Yi4

  • 1Department of Cariology and Endodontology, Peking University School and Hospital of Stomatology, Beijing, China.

International Endodontic Journal
|April 11, 2026
PubMed
Summary

A novel deep learning model accurately measures the distance between the maxillary sinus (MS) and maxillary posterior teeth (MPT) using cone beam computed tomography (CBCT) scans. This automated framework ensures reliable detection across various anatomical differences.

Keywords:
3D point cloudsCBCTdeep learningmaxillary sinusposterior teeth

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Dentistry
  • Computational Anatomy

Background:

  • The spatial relationship between the maxillary sinus (MS) and maxillary posterior teeth (MPT) is crucial for dental implantology and sinus lift procedures.
  • Accurate measurement of the MS-MPT distance is essential for treatment planning and avoiding complications.
  • Traditional methods for assessing this relationship can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automated analysis of the MS-MPT relationship using cone beam computed tomography (CBCT) images.
  • To implement a 3D point cloud algorithm for precise measurement of the distance between the MS and MPT.
  • To compare the automated measurements with clinical ground truth data.

Main Methods:

  • A dataset of 88 MSs and 352 MPTs from CBCT scans was utilized.
  • A U-Net convolutional block attention (CBAM) architecture was employed for segmentation of MSs and MPTs.
  • A 3-fold cross-validation strategy was used to train and assess the segmentation model.
  • Calibrated point clouds were reconstructed for Euclidean distance measurement between MS and MPT, identifying the minimum distance.

Main Results:

  • The segmentation model achieved high accuracy with mean Dice Similarity Coefficients (DSC) of 0.959 for MS and 0.913 for MPT.
  • The automated MS-MPT distance measurements showed strong consistency with clinician-determined ground truth (correlation coefficient ϒ > 0.993, p < 0.01).
  • The model achieved a successful detection rate of 70.3% for the MPT root apex within a 1mm threshold, with a mean signed error of 0.63mm.

Conclusions:

  • An automated framework combining DL segmentation and 3D point cloud analysis was successfully developed.
  • The framework accurately quantifies the MS-MPT relationship, demonstrating reliable detection across diverse anatomical variations in CBCT scans.
  • This automated approach offers a precise and efficient tool for clinical applications in dentistry.