Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study

Yuichi Mine, Yuko Iwamoto, Shota Okazaki, Kentaro Nakamura, Saori Takeda, Tzu Yu Peng, Chieko Mitsuhata, Naoya Kakimoto, Katsuyuki Kozai, Takeshi Murayama

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. Aim: This study aimed to apply convolutional neural network (CNN)–based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. Design: Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. Results: The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. Conclusion: CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.

Original languageEnglish
JournalInternational Journal of Paediatric Dentistry
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • artificial intelligence
  • convolutional neural network
  • deep learning
  • supernumerary teeth
  • transfer learning

ASJC Scopus subject areas

  • Dentistry(all)

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