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Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set.

W Park1,2, F Schwendicke3,4, J Krois3,4

  • 1Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Korea.

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|April 22, 2023
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Summary

Deep learning (DL) accurately identifies and classifies dental implant systems (DISs), outperforming dental professionals in accuracy and speed. This AI tool shows promise as a clinical decision support aid for DIS identification.

Keywords:
artificial intelligencecomputer assisted radiographic image interpretationdentists

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Area of Science:

  • Dental radiology
  • Artificial intelligence in dentistry
  • Medical imaging analysis

Background:

  • Accurate identification of dental implant systems (DISs) is crucial for effective treatment planning and patient care.
  • Current methods rely on manual interpretation by dental professionals, which can be time-consuming and prone to errors.
  • The increasing complexity and variety of DISs necessitate advanced diagnostic tools.

Purpose of the Study:

  • To evaluate the efficacy of deep learning (DL) in identifying and classifying various types of dental implant systems (DISs).
  • To compare the classification accuracy and time efficiency of DL models against dental professionals.
  • To assess the potential of DL as a decision support tool in clinical dental practice.

Main Methods:

  • A large-scale multicenter dataset of 37,442 periapical and 113,291 panoramic radiographic images was utilized.
  • Images represented 10 manufacturers and 25 different types of DISs.
  • A pretrained and modified ResNet-50 architecture was employed for DL classification, with performance compared to specialized and non-specialized dental professionals via questionnaire.

Main Results:

  • Deep learning achieved a classification accuracy of 82.0% (95% CI, 75.9%-87.0%), significantly outperforming dental professionals (mean accuracy: 23.5% ± 18.5%).
  • Specialized implantologists achieved a mean accuracy of 43.3% ± 20.4%, while non-specialized dentists achieved 16.8% ± 9.0%.
  • DL required significantly less time (4.5 min) for classification compared to specialized (75.6 ± 31.0 min) and non-specialized (91.3 ± 38.3 min) dentists.

Conclusions:

  • Deep learning demonstrates reliable outcomes in the identification and classification of diverse dental implant systems.
  • The classification accuracy of DL significantly surpasses that of both specialized and non-specialized dental professionals.
  • DL can be effectively implemented as a decision support aid for identifying and classifying DISs in clinical settings.