Application of deep machine learning for the radiographic diagnosis of periodontitis

Chang, Jennifer; Chang, Ming-Feng; Angelov, Nikola; Hsu, Chih‐Yu; Meng, Hsiu-Wan; Sheng, Sally; Glick, Aaron; Chang, Kearny; He, Yun-Ru; Lin, Yi‐Bing; Wang, Bing-Yan; Ayilavarapu, Srinivas

doi:10.1007/s00784-022-04617-4

Cited by 18 publications

(20 citation statements)

References 34 publications

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“…Mean accuracies of the model were reported to be 0.88 ± 0.03 for the mild and 0.86 ± 0.03 for the severe bone loss group, with no significant difference in accuracy between the two groups ( p = 0.20). 49 Alotaibi et al developed a CNN model based on VGG-16 (Visual Geometry Group) network architecture with the TensorFlow and Keras libraries in Python. The data set consisted of 1724 intraoral periapical images of upper and lower anterior teeth.…”

Section: Discussionmentioning

confidence: 99%

“…The RBL classification was performed by two‐node, and the defect morphology classification was performed by three‐node fully connected layers, both with softmax activation functions. Mean accuracies of the model were reported to be 0.88 ± 0.03 for the mild and 0.86 ± 0.03 for the severe bone loss group, with no significant difference in accuracy between the two groups ( p = 0.20) 49 . Alotaibi et al developed a CNN model based on VGG‐16 (Visual Geometry Group) network architecture with the TensorFlow and Keras libraries in Python.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Vertical root fracture [72,73] Deep learning Periapical pathosis [21], dental tumors [74], tooth numbering [75][76][77][78], tooth detection and identification [79][80][81], periodontal bone loss [32,82,83] Disease classification Classical image analysis approaches Tooth detection [84,85], osteoporosis assessment [86], dental caries [87] Machine learning Dental caries [88], proximal dental caries [14], molar and pre-molar teeth [89], osteoporosis [90], dental caries [15], periapical lesions [16,17], dental restorations [22], periapical roots [91], teeth with root [92], sagittal patterns [93] Deep learning Tooth numbering [94][95][96][97][98][99], dental implant stages [100], implant fixture [101], bone loss [18], periapical periodontitis [102][103][104][105], dental decay [106], approximal dental caries [19] Disease segmentation C...…”

Section: Disease Detection Machine Learningmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

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“…Deep learning (DL) networks may be a useful tool for improving the accuracy and efficiency of evaluating RBL. Several studies have used DL to measure alveolar bone levels on panoramic or periapical radiographs [ 9 – 12 ]. However, many of these studies prioritize classifying the severity of RBL over pinpointing its exact value.…”

Section: Introductionmentioning

confidence: 99%

A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss

Yu,

Ye,

Hong

et al. 2024

BMC Oral Health

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Objective Marginal alveolar bone loss is one of the key features of periodontitis and can be observed via panoramic radiographs. This study aimed to establish a cascading learning method with deep learning (DL) for precise radiographic bone loss (RBL) measurements at specific tooth positions. Materials and methods Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth’s crown, intrabony portion, and suprabony portion of the roots were obtained. The RBL was subsequently measured by length through these three areas using the principal component analysis (PCA) principal axis. Results The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1-score of 0.9338. The average IoU for the tooth semantic segmentation task was 0.8465, with an F1-score of 0.9138. When the two tasks were combined, the average IoU was 0.7889, with an F1-score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians’ measurements exceeded 0.85. Compared to those of the other two methods, the average precision of the predicted RBL was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444. Conclusions The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis. Clinical relevance Precise RBL measurements are important for periodontal diagnosis. The proposed RBL-SF can measure RBL at specific tooth positions and assign the bone loss stage. The ability of the RBL-SF to measure RBL at specific tooth positions can guide clinicians to a certain extent in the accurate diagnosis of periodontitis.

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Application of deep machine learning for the radiographic diagnosis of periodontitis

Cited by 18 publications

References 34 publications

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

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

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss

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