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Original Research

Open Access

Image segmentation of impacted mesiodens using deep learning

  • Hyuntae Kim1
  • Ji-Soo Song2
  • Teo Jeon Shin2
  • Young-Jae Kim2
  • Jung-Wook Kim2
  • Ki-Taeg Jang2
  • Hong-Keun Hyun2,*,

1Department of Pediatric Dentistry, Seoul National University Dental Hospital, 03080 Seoul, Republic of Korea

2Department of Pediatric Dentistry, Dental Research Institute, School of Dentistry, Seoul National University, 03080 Seoul, Republic of Korea

DOI: 10.22514/jocpd.2024.059 Vol.48,Issue 3,May 2024 pp.52-58

Submitted: 10 September 2023 Accepted: 18 October 2023

Published: 03 May 2024

*Corresponding Author(s): Hong-Keun Hyun E-mail: hege1@snu.ac.kr

Abstract

This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3–9 years) was included in this study. The U-Net semantic segmentation algorithm was applied for the detection and segmentation of mesiodens in the upper anterior region. For enhancement of the algorithm, pre-trained ResNet models were applied to the encoding path. The segmentation performance of the algorithm was tested using the Jaccard index and Dice coefficient. The diagnostic accuracy, precision, recall, F1-score and time to diagnosis of the algorithms were compared with those of human expert groups using the test dataset. Cohen’s kappa statistics were compared between the model and human groups. The segmentation model exhibited a high Jaccard index and Dice coefficient (>90%). In mesiodens diagnosis, the trained model achieved 91–92% accuracy and a 94–95% F1-score, which were comparable with human expert group results (96%). The diagnostic duration of the deep learning model was 7.5 seconds, which was significantly faster in mesiodens detection compared to human groups. The agreement between the deep learning model and human experts is moderate (Cohen’s kappa = 0.767). The proposed deep learning algorithm showed good segmentation performance and approached the performance of human experts in the diagnosis of mesiodens, with a significantly faster diagnosis time.


Keywords

Artificial intelligence; Mesiodens; Deep learning; U-Net; Semantic segmentation; Panoramic radiography


Cite and Share

Hyuntae Kim,Ji-Soo Song,Teo Jeon Shin,Young-Jae Kim,Jung-Wook Kim,Ki-Taeg Jang,Hong-Keun Hyun. Image segmentation of impacted mesiodens using deep learning. Journal of Clinical Pediatric Dentistry. 2024. 48(3);52-58.

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