Current applications and development of artificial intelligence for digital dental radiography

Putra, Ramadhan Hardani; Doi, Chiaki; Yoda, Nobuhiro; Astuti, Eha Renwi; Sasaki, Keiichi

doi:10.1259/dmfr.20210197

Cited by 63 publications

(61 citation statements)

References 135 publications

(106 reference statements)

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“…There has been a dramatic increase in the number of DCNN studies in recent years. 12 Various networks have been applied for the detection or classification of lesions or anatomical structures. 8 9 10 11 13 14 15 Several studies have also evaluated the performance of DCNNs in implant fixture classification in a similar way.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, several studies have utilized deep convolutional neural networks (DCNNs) to detect or classify various lesions or objects in dental radiographs. 8 9 10 11 12 13 14 15 DCNNs alone can be trained to show excellent performance, but transfer learning to DCNNs is considered to be more efficient. 8 10 11 The concept of “transfer learning” refers to training neural networks that have already been trained before, with related, but new data.…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Kim

et al. 2022

Imaging Sci Dent

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Purpose This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/ ), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Kim

et al. 2022

Imaging Sci Dent

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“…Periapical radiography allows for the detection of dental caries and periapical lesions, evidenced by radiotransparency. Due to it being a 2D examination, its value is limited in the case of multiple roots [ 62 ]. Panoramic radiography offers an overview of the entire maxilla, with the disadvantage of being less sensitive in detecting periapical lesions [ 63 ].…”

Section: Clinical Manifestationsmentioning

confidence: 99%

Odontogenic Sinusitis: From Diagnosis to Treatment Possibilities—A Narrative Review of Recent Data

et al. 2022

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The maxillary sinus is a structure at the border of specialties: otorhinolaryngology and maxillofacial surgery. Due to this fact, regarding etiology, it can be affected by both the rhinogenic and odontogenic path and can impose diagnostic difficulties. The etiopathogenic mechanisms that can affect the Schneiderian membrane are mainly inflammatory, iatrogenic, traumatic, and tumorous in nature. From a microbiological point of view, the bacteriology is polymorphic, including both aerobic and anaerobic species in acute OS, the predominating species in acute OS being aerobic, and in chronic anaerobic germs. The role of fungi in the determination of this pathology and in the production of the biofilm that leads to resistance to antibiotic treatment is also discussed. The present paper aims to present the etiopathogenesis, bacteriology, clinical manifestations, as well as treatment of odontogenic sinusitis (OS) from an updated perspective through reviewing the literature. If unilateral maxillary sinusitis is usually due to odontogenic causes, this does not clinically exclude the possibility of strictly rhinogenic causes in the occurrence of sinusitis. This underlines the important role of complex oral and rhinological clinical examination as well as the role of preclinical examinations in specifying the certainty diagnosis. Simple radiography, orthopantomography, CT, and CBCT are compared in terms of diagnostic accuracy. The treatment of OS is complex, involving medication, dental, and surgical measures. The value of endoscopic surgery is emphasized, comparing its advantages over the classic Caldwell-Luc technique.

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“…For clinical AI applications in dentistry, a full digital workflow transformation is necessary [ 16 , 17 , 18 , 19 , 20 , 21 ]. Currently, various examples of advanced dental technologies can be found based on digital workflows [ 7 , 16 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Their goal is to enhance the efficiency and quality of delivered services.…”

Section: Introductionmentioning

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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Current applications and development of artificial intelligence for digital dental radiography

Cited by 63 publications

References 135 publications

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Odontogenic Sinusitis: From Diagnosis to Treatment Possibilities—A Narrative Review of Recent Data

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

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