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BMC Oral Health
Published in: BMC Oral Health 1/2023

Open Access 01-12-2023 | Artificial Intelligence | Research

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Authors: İrem Bağ, Elif Bilgir, İbrahim Şevki Bayrakdar, Oğuzhan Baydar, Fatih Mehmet Atak, Özer Çelik, Kaan Orhan

Published in: BMC Oral Health | Issue 1/2023

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Abstract

Background

Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence.

Methods

A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set.

Results

A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92).

Conclusions

The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.
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Metadata
Title
An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population
Authors
İrem Bağ
Elif Bilgir
İbrahim Şevki Bayrakdar
Oğuzhan Baydar
Fatih Mehmet Atak
Özer Çelik
Kaan Orhan
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-023-03532-8

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