Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs

Engels, Paula; Meyer, Ole; Schönewolf, Jule; Schlickenrieder, Anne; Hickel, Reinhard; Hesenius, Marc; Gruhn, Volker; Kühnisch, Jan

doi:10.1016/j.jdent.2022.104124

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…Apart from studies on PRs, some studies have measured the success of fxation on intraoral images [22,23]. Engels et al [22] aimed to detect and categorize dental restorations.…”

Section: Discussionmentioning

confidence: 99%

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Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Altan

Güneç

ÇINAR

et al. 2022

Scientific Programming

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Aim. This study applied a CNN (convolutional neural network) algorithm to detect prosthetic restorations on panoramic radiographs and to automatically detect these restorations using deep learning systems. Materials and Methods. This study collected a total of 5126 panoramic radiographs of adult patients. During model training, .bmp, .jpeg, and .png files for images and .txt files containing five different types of information are required for the labels. Herein, 10% of panoramic radiographs were used as a test dataset. Owing to labeling, 2988 crowns and 2969 bridges were formed in the dataset. Results. The mAP and mAR values were obtained when the confidence threshold was set at 0.1. TP, FP, FN, precision, recall, and F1 score values were obtained when the confidence threshold was 0.25. The YOLOv4 model demonstrated that accurate results could be obtained quickly. Bridge results were found to be more successful than crown results. Conclusion. The detection of prosthetic restorations with artificial intelligence on panoramic radiography, which is widely preferred in clinical applications, provides convenience to physicians in terms of diagnosis and time management.

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“…Apart from studies on PRs, some studies have measured the success of fxation on intraoral images [22,23]. Engels et al [22] aimed to detect and categorize dental restorations.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from studies on PRs, some studies have measured the success of fxation on intraoral images [22,23]. Engels et al [22] aimed to detect and categorize dental restorations. Te diagnostic accuracy was 97.8% for ceramic restorations and 99.4 for gold restorations in studies in which 1761 images were used.…”

Section: Discussionmentioning

confidence: 99%

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Altan

Güneç

ÇINAR

et al. 2022

Scientific Programming

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“…In the literature, detection of lesions such as squamous cell carcinoma [26], lichen planus [43] using AI systems in intraoral photographs; detection of dental applications such as dental prostheses, restorations and ssure sealants [29,44,45]; in addition, there are many studies on the detection of conditions such as dental caries [28, 46], white spot [18], and anomalies such as microdontia, rotation, and supernumerary [47]. All of these studies about AI, which has attracted great interest in dentistry in recent years, support the usability of these systems for intraoral photographs and dental cameras in the dental eld in the coming years.…”

Section: Discussionmentioning

confidence: 99%

“…(2022) had reported that AI systems showed success in the range of 92.9-99.2% in the determination of different restorations such as non-restorative tooth, composite restoration, cement restoration, amalgam restoration, gold restoration and ceramic restoration [44]. On the other hand, Schlickenrieder et al ( 2021) had reported the usability of CNN systems in the determination of ssure sealant in intraoral photographs.…”

Section: Discussionmentioning

confidence: 99%

Detection of Tooth Numbering, Frenulum, Gingival Hyperplasia and Gingival Inflammation on Dental Photographs Using Convolutional Neural Network Algorithms: An Initial Study

Bayrakdar

Uğurlu

Yavuz

et al. 2022

Preprint

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Objectives The aim of this study is to perform tooth numbering using deep learning algorithms on digital dental photographs, and to evaluate the success of these algorithms in determining the presence of frenulum, gingival hyperplasia and gingival inflammation which play an important role in periodontal treatment planning. Materials and Methods Six-hundred-fifty-four (n = 654) intraoral photographs were included in the study. A total of 16795 teeth in all photographs were segmented and the numbering of the teeth was carried out according to the FDI system. Two-thousand-four-hundred-and-ninety-three frenulum attachments (n = 2493), 1211 gingival hyperplasia areas and 2956 gingival inflammation areas in the photographs were labeled using the segmentation method. Images were sized before artificial intelligence (AI) training and data set was separated as training, validation and test groups. Yolov5 architecture were used in the creation of the models. The confusion matrix system and ROC analysis were used in the statistical evaluation of the results. Results When results of study were evaluated; sensitivity, precision, F1 score and AUC for tooth numbering were 0.990, 0.784, 0.875, 0.989; for frenulum attachments were 0.894, 0.775, 0.830 and 0.827; for gingival hyperplasia were 0.757, 0.675, 0.714, 0.774; for gingival inflammation were 0.737, 0.823, 0.777, 0.802 (respectively). Conclusions There is a need for more comprehensive studies to be carried out on this subject by increasing the number of data and the number of parameters evaluated. Clinical relevance The current study showed that in the future, periodontal problem determination from dental photographs could be performed using AI systems.

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“…In education, AI's data analytical capabilities have catalyzed progress in dentistry. It expedites treatments, enhances diagnostic precision, and facilitates prognosis predictions (Chau et al, 2022;Engels et al, 2022;Keser et al, 2022;Lee S.J. et al, 2022;Mine et al, 2022;Sakai et al, 2023), benefiting both dental professionals and patients (Laurenza et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Decoding AI Curricular Integration: Navigating Course Quality in Brazilian Dental Master's Programs

Dantas,

Silva

2023

Preprint

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The integration of Artificial Intelligence (AI) concepts into dental education is a critical transformation that has the potential to revolutionize patient care. However, the understanding of the extent to which AI concepts have been integrated into dental master's programs in Brazil is limited. This study aimed to evaluate the presence of AI-related content in Brazilian master’s degree programs for Dental Professionals. Using official data, from the Brazilian Government 92 master programs were analysed. Within the 2161 disciplines evaluated it was observed that only 13 courses, usually higher quality scored courses (p < 0.001) offered AI-related content. Besides, no correlations were demonstrated between the offering of AI-related content and the course's geographic location (p > 0.05), type of master course (academic or professional) (p > 0.05), and whether it was offered from a public or private institution (p > 0.05). It was concluded that the integration of AI-related content into Brazilian dental master's programs is limited, and usually related to higher quality scored courses. While demonstrating the impacts of the course quality on its innovative method this study emphasizes the urge for a multidimensional approach ensuring an effective integration of AI in the dental profession in Brazil.

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Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs

Cited by 13 publications

References 31 publications

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Detection of Tooth Numbering, Frenulum, Gingival Hyperplasia and Gingival Inflammation on Dental Photographs Using Convolutional Neural Network Algorithms: An Initial Study

Decoding AI Curricular Integration: Navigating Course Quality in Brazilian Dental Master's Programs

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