Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence

Chen, Chin-Chang; Wu, Yifan; Aung, Lwin Moe; Lin, Jerry Chin-Yi; Ngo, Sin Ting; Su, Jo-Ning; Lin, Yuan-Min; Chang, Wei‐Jen

doi:10.1016/j.jds.2023.03.020

Cited by 15 publications

(20 citation statements)

References 25 publications

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“…After screening the titles and abstracts of the 1267 remaining studies, 49 articles were selected for full-text reading. Based on the inclusion and exclusion criteria, 27 studies were included in this systematic review [ 20 , 21 , 28 – 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…2 , respectively. Nearly half of the included studies did not have clear information on whether patients were consecutively or randomly enrolled, resulting in 42.9% of the articles (12/27) showing an unclear risk of bias in the patient selection domain [ 20 , 30 , 32 , 34 – 36 , 38 , 45 , 48 , 52 , 37 , 42 ]. Two studies were rated as having a high risk of bias, with one [ 29 ] designed to be a case-control study with a convenient sample collection and the other [ 31 ] using inappropriate exclusion criteria.…”

Section: Resultsmentioning

confidence: 99%

“…Four studies were unable to accurately diagnose periodontitis based on their reference tests, as these studies attempted to classify healthy cases and periodontitis only using radiographs [ 21 , 28 , 42 , 49 ]. The other studies (85.2%, 23/27) were ranked as having a low risk of bias in the reference standard domain [ 20 , 29 – 41 , 43 – 48 , 50 – 52 ]. As the diagnostic tests are being conducted by DL algorithms, which do not affect the flow and timing, all articles in the present analysis were ranked as low risk.…”

Section: Resultsmentioning

confidence: 99%

“…Jaiswal et al developed a novel Intelligent Ant Lion-based Convolution Neural Model (IALCNM) to segment affected parts and classify the wear and periodontitis using panoramic photographs [ 19 ]. Moreover, Chen et al developed an ensembled CNN model to predict tooth position and recognize radiographic bone loss (RBL) using periapical and bitewing radiographs [ 20 ]. Furthermore, Moran et al evaluated whether different pre-processing methods affect the result of periodontal bone loss (PBL) classification based on periapical images [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

Li,

Zhao,

Xie

et al. 2023

BMC Oral Health

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Background The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. Methods A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. Results Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82–0.92), 0.82 (95%CI 0.72–0.89), 4.9 (95%CI 3.2–7.5), 0.15 (95%CI 0.10–0.22) and 33 (95%CI 19–59), respectively. The area under the SROC was 0.92 (95%CI 0.89–0.94). Conclusions The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

Li,

Zhao,

Xie

et al. 2023

BMC Oral Health

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“…In 2023, Chen et al developed an ensemble model utilizing the YOLOv5 and VIA labeling platform, including VGG‐16 and U‐Net architecture, to detect tooth position, tooth shape, periodontal bone level detection, and RBL in periapical and bitewing radiographs. The accuracy of RBL detection was reported to be 97.0%, while the overall accuracy of the model was reported to be approximately 90% 51 …”

Section: Discussionmentioning

confidence: 99%

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

Amasya,

Jaju,

Ezhov

et al. 2023

Int J Imaging Syst Tech

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This retrospective study is aimed at developing a web‐based artificial intelligence (AI) software (DiagnoCat) for periodontal bone loss detection on panoramic radiographs and evaluating the model's performance by comparing it with clinicians' results. Separate models are trained for tooth and periodontal bone loss detection. The first model's objective was to detect teeth, segmenting their masks, and to define their numbering and developed with Mask R‐CNN using pretrained ResNet‐101 as a backbone. The second model was based on Cascade R‐CNN architecture and used for bone loss prediction. Around 100 radiographs are evaluated by three clinicians regarding tooth identification and periodontal bone loss, separately. Ground truth is determined by the consensus and model's performance is evaluated with kappa, precision, recall, and F‐score statistics. For tooth conditions, the overall F‐score, accuracy, and Cohen's kappa coefficients were found to be 0.948, 0.977, and 0.933 for the binary, and 0.992, 0.988, and 0.961 for the multiclass results. For bone loss detection, the overall F‐score, accuracy, and Cohen's kappa coefficients were found to be 0.985, 0.980, and 0.956 for the binary, and 0.996, 0.993, and 0.974 for the multiclass results. The results of this study suggest that the use of a web‐based AI software (DiagnoCat) can be beneficial in detecting periodontal bone loss on panoramic radiographs.

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Outlook for AI in Oral Surgery and Periodontics

Kurt-Bayrakdar,

Orhan,

Jagtap

2023

Artificial Intelligence in Dentistry

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Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence

Cited by 15 publications

References 25 publications

Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

Outlook for AI in Oral Surgery and Periodontics

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