Automatic and visualized grading of dental caries using deep learning on panoramic radiographs

Chen, Qingguang; Huang, Junchao; Zhu, Haihua; Lian, Luya; Wei, Kaihua; Lai, Xiaomin

doi:10.1007/s11042-022-14089-z

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Risk of bias: Six studies (17%) had a high risk of bias in patient selection [40,50,51,54,58,71] mostly due to the lack of the rationale of its sample size and the unspecified data source. Most of the studies had a high risk of bias for index test (94%), which was most often due to the lack of validation or testing on external data.…”

Section: Methodological Quality Of the Included Studies Checklist For...mentioning

confidence: 99%

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

Ndiaye,

Gasqui,

Millioz

et al. 2024

Caries Res

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Introduction: A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries. Methods: The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. Results: After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model’s performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. Conclusion: This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.

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Section: Methodological Quality Of the Included Studies Checklist For...mentioning

confidence: 99%

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

Ndiaye,

Gasqui,

Millioz

et al. 2024

Caries Res

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Automatic oral cancer detection and classification using modified local texture descriptor and machine learning algorithms

Yaduvanshi,

Murugan,

Goel

2024

Multimed Tools Appl

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Clinical Application of Deep Learning for Enhanced Multistage Caries Detection in Panoramic Radiographs

Pornprasertsuk-Damrongsri,

Vachmanus,

Papasratorn

et al. 2024

Preprint

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The detection of dental caries is typically overlooked on panoramic radiographs. This study aims to leverage deep learning to identify multistage caries on panoramic radiographs. The panoramic radiographs were confirmed with the gold standard bitewing radiographs to create a reliable ground truth. The dataset of 500 panoramic radiographs with corresponding bitewing confirmations was labelled by an experienced and calibrated radiologist for 1,792 caries from 14,997 teeth. The annotations were stored using the annotation and image markup standard to ensure consistency and reliability. The deep learning system employed a two-model approach: YOLOv5 for tooth detection and Attention U-Net for segmenting caries. The system achieved impressive results, demonstrating almost perfect agreement with dentists for both caries counts and classifications (enamel, dentine, and pulp). However, some discrepancies exist, particularly in underestimating enamel caries. While the model occasionally overpredicts caries in healthy teeth (false positive), it prioritizes minimizing missed lesions (false negative), achieving a high recall of 0.96. Overall performance surpasses previously reported values, with an F1-score of 0.85 and an accuracy of 0.93 for caries segmentation in posterior teeth. The deep learning approach demonstrates promising potential to aid dentists in caries diagnosis, treatment planning, and dental education.

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Automatic and visualized grading of dental caries using deep learning on panoramic radiographs

Cited by 3 publications

References 49 publications

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

Automatic oral cancer detection and classification using modified local texture descriptor and machine learning algorithms

Clinical Application of Deep Learning for Enhanced Multistage Caries Detection in Panoramic Radiographs

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