Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm

Duman, Sacide; Yilmaz, Emir; Eşer, Gözde; Çelik, Özer; Bayrakdar, İ̇brahim Şevki; Bilgir, Elif; Costa, André Luiz Ferreira; Jagtap, Rohan; Orhan, Kaan

doi:10.1007/s11282-022-00622-1

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…The demonstration shows that CNN models can achieve an accuracy of 82 to 88% on a very small dataset, which is consistent with several previous studies that demonstrated the superiority of image augmentation for small datasets. 16 30 Among the three models, the Xception model performed the best in terms of both accuracy and F1-score. The Xception model was designed in 2017 to provide higher accuracy than previous CNNs, including ResNet152V2, in the ImageNet data classification task.…”

Section: Discussionmentioning

confidence: 99%

“…24 AI techniques using CNN for disease diagnosis (classification) have been studied in many fields using radiography, clinical examination, or histopathology. [7][8][9][10][11][12][13][14][15][16][17][18] The application of CNN in diagnosing skin lesions based on the clinical appearance and color has been studied previously. 25,26 Skin and oral mucosal lesions share similar diagnostic principles.…”

Section: Discussionmentioning

confidence: 99%

“…12 Furthermore, histopathological diagnosis of oral squamous cell carcinoma using CNN has a sensitivity of 98% and specificity of 92%. 13 The application of CNN using panoramic radiographs has also shown significant results, including diagnosis of radiolucent lesions, 14 mesiodens, 15 taurodontism, 16 cystic lesions, 17 or even fractures. 18…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

et al. 2023

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Objective The aim of this study was to employ artificial intelligence (AI) via convolutional neural network (CNN) for the separation of oral lichen planus (OLP) and non-OLP in biopsy-proven clinical cases of OLP and non-OLP. Materials and Methods Data comprised of clinical photographs of 609 OLP and 480 non-OLP which diagnosis has been confirmed histopathologically. Fifty-five photographs from the OLP and non-OLP groups were randomly selected for use as the test dataset, while the remaining were used as training and validation datasets. Data augmentation was performed on the training dataset to increase the number and variation of photographs. Performance metrics for the CNN model performance included accuracy, positive predictive value, negative predictive value, sensitivity, specificity, and F1-score. Gradient-weighted class activation mapping was also used to visualize the important regions associated with discriminative clinical features on which the model relies. Results All the selected CNN models were able to diagnose OLP and non-OLP lesions using photographs. The performance of the Xception model was significantly higher than that of the other models in terms of overall accuracy and F1-score. Conclusions Our demonstration shows that CNN models can achieve an accuracy of 82 to 88%. Xception model performed the best in terms of both accuracy and F1-score.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

et al. 2023

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“…Several studies have reported that artificial intelligence helps in image reading, and that it helps clinicians 20,21 . In the field of dentistry, artificial intelligence research using panoramic photos has been actively conducted recently; most studies have been on tooth segmentation and tooth number matching [22][23][24] , detection of primary teeth 25 , and detection of taurodontism 26 , which is a tooth anomaly. Studies on artificial intelligence detection of osteoporosis in panoramic photos are also being actively conducted [27][28][29] .…”

Section: Discussionmentioning

confidence: 99%

Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography

Song

Jeong²,

Kim

et al. 2022

Sci Rep

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Artificial intelligence (AI) is limited to teeth and periodontal disease in the dental field, and is used for diagnosis assistance or data analysis, and there has been no research conducted in actual clinical situations. So, we created an environment similar to actual clinical practice and conducted research by selecting three of the soft tissue diseases (carotid artery calcification, lymph node calcification, and sialolith) that are difficult for general dentists to see. Therefore, in this study, the accuracy and reading time are evaluated using panoramic images and AI. A total of 20,000 panoramic images including three diseases were used to develop and train a fast R-CNN model. To compare the performance of the developed model, two oral and maxillofacial radiologists (OMRs) and two general dentists (GDs) read 352 images, excluding the panoramic images used in development for soft tissue calcification diagnosis. On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. The diagnostic accuracy and specificity for soft tissue calcification of AI were high from 0.727 to 0.926 and from 0.171 to 1.000, whereas the sensitivity for lymph node calcification and sialolith were low at 0.250 and 0.188, respectively. The reading time of AI increased in the GD group (619 to 1049) and decreased in the OMR group (1347 to 1372). In addition, reading scores increased in both groups (GD from 11.4 to 39.8 and OMR from 3.4 to 10.8). Using AI, although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the total reading time of the OMR specialists was reduced and the GDs reading accuracy was improved. The AI used in this study helped to improve the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification diagnosis, but more data sets are needed to improve the detection performance of the two diseases with low sensitivity of AI.

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“…Most of the studies are aimed at evaluating the diagnostic performance of deep learning models developed for segmentation, classification, or anatomic landmark detection. Numerous studies demonstrated that diagnosis using artificial intelligence models, such as CNN architectures, is very promising especially in carious lesion detection [ 9 , 12 , 13 , 14 , 15 ], periodontal disease detection [ 16 , 17 ], cephalometric analysis [ 18 ], periapical lesion detection [ 19 ], detection of atherosclerotic carotid plaques [ 20 ], detection of taurodont teeth [ 21 ], and segmentation and classification of Sella turcica [ 22 ] findings.…”

Section: Introductionmentioning

confidence: 99%

Automatic Feature Segmentation in Dental Periapical Radiographs

et al. 2022

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While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

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Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm

Cited by 10 publications

References 29 publications

Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography

Automatic Feature Segmentation in Dental Periapical Radiographs

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