An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs

Yaşa, Yasin; Çelik, Özer; Bayrakdar, İ̇brahim Şevki; Pekince, Adem; Orhan, Kaan; Akarsu, Serdar; Atasoy, Samet; Bilgir, Elif; Odabaş, Alper; Aslan, Ahmet Faruk

doi:10.1080/00016357.2020.1840624

Cited by 47 publications

(33 citation statements)

References 37 publications

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“…CNN-based AI algorithms can be beneficial to the dentist as clinical decision support systems [ 114 ]. A deep CNN system can be used to number teeth in bitewing radiographs and save the dentist time by automatically preparing dental charts [ 115 ]. AI models may serve as powerful tools in the diagnosis of dental caries.…”

Section: Resultsmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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Section: Resultsmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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“…Most research on deep learning performance of dental diagnosis has used intraoral radiography that provides more detailed information about the relevant region. [16][17][18][19] . However, panoramic radiography has gain of high research interest due to the allowing a single annotated image that include all teeth, vast number of anatomical structures and possible pathologies 20 .…”

Section: Discussionmentioning

confidence: 99%

Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs

Kaya

Güneç

Gökyay

et al. 2022

JOCPD

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In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs. Study Design: YOLO V4, a CNN (Convolutional Neural Networks) based object detection model was used for automated tooth detection and numbering. 4545 pediatric panoramic X-ray images, processed in labelImg, were trained and tested in the Yolo algorithm. Results and Conclusions: The model was successful in detecting and numbering both primary and permanent teeth on pediatric panoramic radiographs with the mean average precision (mAP) value of 92.22 %, mean average recall (mAR) value of 94.44% and weighted-F1 score of 0.91. The proposed CNN method yielded high and fast performance for automated tooth detection and numbering on pediatric panoramic radiographs. Automatic tooth detection could help dental practitioners to save time and also use it as a pre-processing tool for detection of dental pathologies.

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“…Bitewing images are commonly used to number a tooth employing artificial intelligence [ 5 , 14 ]. Chen et al [ 5 ] employed a faster R-CNN to number teeth in periapical images.…”

Section: Discussionmentioning

confidence: 99%

“…Yasa et al [ 14 ] analyzed 1,125 bitewing images with a faster R-CNN with the goal of identifying and number teeth. The proposed neural network achieved a precision of 0.9293 in tooth numbering.…”

Section: Discussionmentioning

confidence: 99%

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images

Prados-Privado

Villalón²,

Torres³

et al. 2021

BioMed Research International

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Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.

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An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs

Cited by 47 publications

References 37 publications

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images

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