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Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation.

Baoxin Tao1,2,3,4,5,6, Jiangchang Xu7, Jie Gao1,2,3,4,5,6

  • 1Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Clinical Oral Implants Research
|November 30, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning accurately segments graft materials in sinus augmentation (SA) using cone-beam computed tomography (CBCT) scans. This AI model significantly outperforms manual segmentation by surgeons in speed and precision.

Keywords:
artificial intelligencedeep learningdigital dentistryneural networkssinus augmentation

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Area of Science:

  • Biomedical imaging
  • Artificial intelligence in dentistry
  • Surgical planning

Background:

  • Maxillary sinus augmentation (SA) is crucial for dental implant placement.
  • Accurate volumetric assessment of graft materials is essential for treatment success.
  • Manual segmentation of graft materials from cone-beam computed tomography (CBCT) is time-consuming and subjective.

Purpose of the Study:

  • To evaluate the accuracy and reliability of deep learning for automatic graft material segmentation.
  • To compare the performance of a deep learning model against manual segmentation by experienced surgeons.

Main Methods:

  • A deep learning model (3D V-Net and 3D Attention V-Net) was developed using 100 paired CBCT scans.
  • Ground truths were established by consensus among two surgeons and a computer engineer.
  • Model performance was assessed using Dice coefficient, Hausdorff distance, and average surface distance on a test set.

Main Results:

  • The deep learning model achieved a Dice coefficient of 90.36% ± 2.53%.
  • The model demonstrated superior accuracy with a 95% Hausdorff distance of 1.59 ± 0.82 mm and an average surface distance of 0.38 ± 0.11 mm.
  • Automated segmentation took 7.2 seconds, compared to 19.15 minutes for manual segmentation by surgeons.

Conclusions:

  • Deep learning provides a highly accurate and efficient method for graft material segmentation after SA.
  • The model's performance surpasses that of experienced surgeons.
  • This technology can enhance volumetric change evaluation, implant planning, and digital dentistry workflows.