Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado, María; Villalón, Javier García; Martínez-Martínez, Carlos Hugo; Ivorra, Carlos

doi:10.3390/app10082856

Cited by 30 publications

(10 citation statements)

References 46 publications

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“…The first step for the use of neural networks in the field of dentistry is the correct detection of teeth, and for this reason this step must be as reliable and accurate as possible [ 20 ]. A previous literature review showed several studies that employed neural networks for teeth detection [ 21 ], which employed different type of images and neural networks.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have employed different neural network architectures to detect teeth with different type of images and obtain diverse accuracy results. According to previous reviews, the most common neural network employed to detect teeth are Mask R-CNN and faster R-CNN [ 21 ]. Jader et al [ 28 ] employed a mask region based convolutional neural network (Mask R-CNN) to obtain the profile of each tooth employing a database of 1500 panoramic X-ray radiographies.…”

Section: Discussionmentioning

confidence: 99%

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A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

Prados-Privado

Villalón²,

Torres³

et al. 2021

JCM

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Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

Prados-Privado

Villalón²,

Torres³

et al. 2021

JCM

Self Cite

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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%

“…They often overlap various dental specialties and categorizations. Currently, they mostly include: AI in X-ray and other diagnostics, caries [ 29 , 30 , 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] AI in implant dentistry [ 26 , 33 , 45 , 46 ] AI in photography analysis [ 27 , 28 , 29 , 47 ] AI in practice management, tele-dentistry, patient coaching [ 44 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] AI in clinical predictions (virtual simulation, aging, growth) [ 5 , 55 , 56 , 57 , 58 , 59 ] …”

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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“…Deep learning is increasingly beneficial in dentistry with the ability to process big data for analyzing, diagnosing, and disease monitoring [ 7 ]. Convolutional neural networks (CNN) are one of the types of deep neural networks specialized to deal with radiographic images [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal

et al. 2022

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The study aimed to generate a fused deep learning algorithm that detects and classifies the relationship between the mandibular third molar and mandibular canal on orthopantomographs. Radiographs (n = 1880) were randomly selected from the hospital archive. Two dentomaxillofacial radiologists annotated the data via MATLAB and classified them into four groups according to the overlap of the root of the mandibular third molar and mandibular canal. Each radiograph was segmented using a U-Net-like architecture. The segmented images were classified by AlexNet. Accuracy, the weighted intersection over union score, the dice coefficient, specificity, sensitivity, and area under curve metrics were used to quantify the performance of the models. Also, three dental practitioners were asked to classify the same test data, their success rate was assessed using the Intraclass Correlation Coefficient. The segmentation network achieved a global accuracy of 0.99 and a weighted intersection over union score of 0.98, average dice score overall images was 0.91. The classification network achieved an accuracy of 0.80, per class sensitivity of 0.74, 0.83, 0.86, 0.67, per class specificity of 0.92, 0.95, 0.88, 0.96 and AUC score of 0.85. The most successful dental practitioner achieved a success rate of 0.79. The fused segmentation and classification networks produced encouraging results. The final model achieved almost the same classification performance as dental practitioners. Better diagnostic accuracy of the combined artificial intelligence tools may help to improve the prediction of the risk factors, especially for recognizing such anatomical variations.

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Dental Images Recognition Technology and Applications: A Literature Review

Cited by 30 publications

References 46 publications

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

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

A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal

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