Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

Hu, Ziyang; Cao, Dantong; Hu, Yanni; Wang, Baixin; Zhang, Yifan; Tang, Rong; Jia, Zhang; Gao, Antian; Chen, Ying; Lin, Zitong

doi:10.1186/s12903-022-02422-9

Cited by 18 publications

(16 citation statements)

References 42 publications

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“…Deep learning has garnered considerable interest in medicine owing to its high learning capacity and demonstrated ability to automate intricate diagnostic and treatment planning tasks. In endodontics, deep learning has shown promise in detecting vertical root fracture [ 3 ] and special tooth anatomies like C-shaped canals [ 27 ], and taurodontism [ 28 ]. They may also assist in gauging treatment complexity to inform planning and referral needs.…”

Section: Discussionmentioning

confidence: 99%

“…Root canal treatment involves cleaning, shaping and obturation of the root canal system to prevent or treat apical periodontitis [ 1 ]. Despite relatively high success rates (82–92%) [ 2 ], endodontic treatment still carries risks of failure that can be influenced by procedural errors and mishaps [ 3 ]. Studies have demonstrated that errors including apical perforation, failing to achieve patency due to ledges or blockages, and improper obturation length can significantly reduce success rates [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for determining the difficulty of endodontic treatment: a pilot study

Karkehabadi,

Khoshbin,

Ghasemi

et al. 2024

BMC Oral Health

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Background To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs. Methods A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the “simple assessment” criteria from the American Association of Endodontists’ case difficulty assessment form in the Endocase application. A classification task labeled cases as “easy” or “hard”, while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set. Results The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability. Conclusion This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning for determining the difficulty of endodontic treatment: a pilot study

Karkehabadi,

Khoshbin,

Ghasemi

et al. 2024

BMC Oral Health

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“…In the field of image recognition, convolutional neural networks (CNNs) are usually ideal choices with existing methods having achieved over 80% Top-1 accuracy and over 95% Top-5 accuracy on ImageNet datasets [ 14 ], while also demonstrating excellent performance on medical image analysis [ 15 ]. Nowadays, the identification of objects in the image has been applied to a variety of aspects from daily photos to microscopic images [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning in isolated tooth identification

Li,

Wang,

Chen

et al. 2024

BMC Oral Health

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Background Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs. Methods A collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities. Results This deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen’s Kappa demonstrated good agreement between model prediction and the test set. Conclusions This deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth.

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“…14 Previous studies have demonstrated that the performance of CNN models on caries and root fracture detections exhibited 80%-96% accuracy (equivalent to dentists/radiologists). 15,16 In implant dentistry, AI has been investigated for identifying anatomical landmarks 17 and dental implant brands 18 and predicting implant prognosis based on radiographic images. 19 While the condition of the edentulous ridge varies for each implant site, this study aimed to develop an AI model to automatically identify the deficiency of edentulous ridge from the cone-beam computed tomographic (CBCT) images and suggest adequate ridge classification.…”

Section: Introductionmentioning

confidence: 99%

“…Convolutional neural network (CNN), a category of deep learning (DL) AI algorithms, is particularly useful for image‐based learning as it can capture patterns and classify images without labeling manual characteristics 14 . Previous studies have demonstrated that the performance of CNN models on caries and root fracture detections exhibited 80%–96% accuracy (equivalent to dentists/radiologists) 15,16 . In implant dentistry, AI has been investigated for identifying anatomical landmarks 17 and dental implant brands 18 and predicting implant prognosis based on radiographic images 19 …”

Section: Introductionmentioning

confidence: 99%

Deep learning for the identification of ridge deficiency around dental implants

Lin,

Wang,

et al. 2023

Clin Implant Dent Rel Res

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ObjectivesThis study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone‐beam computerized tomography (CBCT).Materials and methodsSingle slices crossing the central long‐axis of 630 mandibular and 845 maxillary virtually placed implants (4–5 mm diameter, 10 mm length) in 412 patients were used. The ridges were classified based on the intraoral bone‐implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet‐50 architecture was employed for DL.ResultsThe model achieved an accuracy of >98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91 ± 1.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal‐vertical defect. On the maxilla, model accuracy was 98.82 ± 1.11%, and the ridge presenting an implant collar‐sinus floor distance of 5–10 mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve >90% model accuracy, ≥441 mandibular slices or ≥592 maxillary slices were required.ConclusionsThe ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3‐dimensional implant‐ridge relationships.

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Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

Cited by 18 publications

References 42 publications

Deep learning for determining the difficulty of endodontic treatment: a pilot study

Deep learning for determining the difficulty of endodontic treatment: a pilot study

Application of deep learning in isolated tooth identification

Deep learning for the identification of ridge deficiency around dental implants

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