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U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in

Hamit Tunç1, Nurullah Akkaya2, Berkehan Aykanat1

  • 1Department of Paediatric Dentistry, Faculty of Dentistry, Burdur Mehmet Akif Ersoy University, 15100 Burdur, Turkey.

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Summary

A new deep learning model accurately segments teeth and detects agenesis in panoramic radiographs for improved pediatric dental diagnostics. This AI tool assists clinicians by reducing errors and workload.

Keywords:
agenesis detectionartificial intelligencedeep learningmaxillofacial radiologymixed dentitionpanoramic radiography

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Deep Learning Architectures

Background:

  • Panoramic radiographs are crucial for pediatric dental diagnosis.
  • Diagnostic errors, particularly those related to tooth overlap and interpretation, remain a challenge.
  • Artificial intelligence (AI) shows promise in enhancing diagnostic accuracy.

Purpose of the Study:

  • To develop a U-Net-based deep learning model for simultaneous tooth segmentation and agenesis detection.
  • To differentiate between primary and permanent teeth in panoramic radiographs.
  • To improve diagnostic accuracy and efficiency in pediatric dentistry.

Main Methods:

  • Utilized 1697 panoramic radiographs from public and university archives.
  • Employed manual segmentation by dental experts for ground truth.
  • Trained a U-Net architecture on 80% of data, validating on 10% and testing on 10%.

Main Results:

  • The model achieved high performance on the test set with a Dice score of 0.8773 and F1 score of 0.9027.
  • Dental agenesis was detected in 14.6% of cases, with common sites being mandibular second premolars and maxillary lateral incisors.
  • Validation accuracy reached 96.71%, demonstrating reliable performance.

Conclusions:

  • The developed deep learning model effectively automates tooth segmentation and agenesis detection in panoramic radiographs.
  • High performance metrics indicate potential for reduced diagnostic errors and clinician workload.
  • This AI tool offers enhanced accuracy and efficiency for pediatric dental diagnostics.