Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Bui, Toan Huy; Hamamoto, Kazuhiko; Paing, May Phu

doi:10.3390/e24101358

Cited by 11 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to these methods, hybrid methods combined with two different algorithms were also used. CNN-LSTM [70], CNN-SVM [71], Siamese Network-DenseNet121 [71], and CNN-fuzzy logic [84] are hybrid models using. In a study [74], a swine transformer, one of the transformer types shown to compete with CNNs recently, was used.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

show abstract

Section: Resultsmentioning

confidence: 99%

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…used SVM to automatically segment the tooth region of interest from panoramic radiographs for dental caries diagnosis, revealing that the method had an accuracy of 93.58 %, sensitivity of 93.91 %, and specificity of 93.33 %, indicating promising potential for a wider application. 28 The advancements in these emerging technologies are promising.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in dentistry: A bibliometric analysis from 2000 to 2023

Xie,

Xu,

Zou

et al. 2024

Journal of Dental Sciences

View full text Add to dashboard Cite

“…Various CNN-based structures have been proposed and applied in semantic segmentation, of which fully convolutional networks (FCNs) that have an encoder–decoder structure are a milestone [ 17 ]. Since then, several types of CNN architectures, such as ResNet [ 18 ], ResNext [ 19 ], VGG [ 20 ], and GoogleNet [ 21 ], have been used as encoders and decoders. U-net [ 22 ] is based on FCN [ 23 ] and exhibits the advantages of good performance, low data requirement, and high speed.…”

Section: Introductionmentioning

confidence: 99%

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

Wang,

Liang,

Chang

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.

show abstract

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Cited by 11 publications

References 35 publications

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Artificial intelligence in dentistry: A bibliometric analysis from 2000 to 2023

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

Contact Info

Product

Resources

About