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Original Research

Open Access

Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients

  • Eunjin Kim1,†
  • Jae Joon Hwang2,3,†
  • Bong-Hae Cho2,3
  • Eungyung Lee1,3
  • Jonghyun Shin1,3,*,

1Department of Pediatric Dentistry, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

2Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

3Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, 50612 Yangsan, Republic of Korea

DOI: 10.22514/jocpd.2024.062 Vol.48,Issue 3,May 2024 pp.76-85

Submitted: 24 August 2023 Accepted: 21 September 2023

Published: 03 May 2024

*Corresponding Author(s): Jonghyun Shin E-mail:

† These authors contributed equally.


Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric patients and to assess the accuracy. The study included patients aged 8–16 years who visited the Pusan National University Dental Hospital and underwent panoramic radiography. A total of 806 panoramic radiographs were retrospectively analyzed to determine the presence or absence of missing teeth for each tooth number. Moreover, each panoramic radiograph was divided into four quadrants, each of a smaller size, containing both primary and permanent teeth, generating 3224 data. Quadrants with missing teeth (n = 1457) were set as the experimental group, and quadrants without missing teeth (n = 1767) were set as the control group. The data were split into training and validation sets in a 4:1 ratio, and a 5-fold cross-validation was conducted. A gradient-weighted class activation map was used to visualize the deep learning model. The average values of sensitivity, specificity, accuracy, precision, recall and F1-score of this deep learning model were 0.635, 0.814, 0.738, 0.730, 0.732 and 0.731, respectively. In the experimental group, the accuracy was the highest for missing canines and premolars, and the lowest for molars. The deep learning model exhibited a moderate to good distinguishing power with a classification performance of 0.730. This deep learning model and the newly defined small sized region of interest proved adequate for classifying the presence of missing teeth.


Missing teeth; Deep learning; Region of interest; Pediatric and adolescent; Panorama

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Eunjin Kim,Jae Joon Hwang,Bong-Hae Cho,Eungyung Lee,Jonghyun Shin. Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients. Journal of Clinical Pediatric Dentistry. 2024. 48(3);76-85.


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