Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs

Chandrashekar, Geetha; AlQarni, Saeed; Bumann, Erin E.; Lee, Yugyung

doi:10.1016/j.compbiomed.2022.105829

Cited by 47 publications

(21 citation statements)

References 22 publications

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“…As noted above, we are currently working to further improve the accuracy of the model by addressing the relative importance of the different segmentation data and including lower-quality images, as well as images from patients with abnormal dentition. We are also working to include the identification of specific tooth numbers to improve the segmentation and classification of the teeth (Chandrashekar et al 2022). Finally, while the present study included various types of fillings, no other dental restorations were evaluated.…”

Section: Discussionmentioning

confidence: 99%

A Novel Collaborative Learning Model for Teeth and Fillings in Radiographs

Bumann

AlQarni

Chandrashekar

et al. 2023

Preprint

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It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients’ radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth, as well as their associated dental fillings. We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance. Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings.

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Section: Discussionmentioning

confidence: 99%

A Novel Collaborative Learning Model for Teeth and Fillings in Radiographs

Bumann

AlQarni

Chandrashekar

et al. 2023

Preprint

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“…Otherwise, model will not be able to judge and execute. In [8], a collaborative model dynamically constructed is used to integrate two tooth segmentation and recognition models. This method has also been effectively proven to be more potent.…”

Section: Discussionmentioning

confidence: 99%

“…Panoramic (PANO) X-ray film is one of the dental X-rays commonly used in daily dental examinations. Compared to other dental X-ray films, it has the important advantage of covering most anatomical structures and clinical findings in a single image [8]. This important feature facilitates analysis by PANO experts and provides important information related to clinical diagnosis and treatment [9].…”

Section: Introductionmentioning

confidence: 99%

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Chen

Huang

et al. 2022

IEEE Access

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Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0.

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“…(to be continued on next page) (533/59/-) PAN 3 dentists (+10y) yes [77] (1250/-/125) PERI dentists (unspecified) yes [81] (3293/252/141) BTW 4 dentists (+3y) yes [82] (492/65/64) BTW 1 dentist + 1 radiologist (+9y) yes [15] (800/-/200) BTW 2 dentists (+10y) yes [83] photos + 120 x-rays * [57] Photos + X-Rays 2 dentists (+3y) yes [1] (1071/-/89) PAN 4 dentists (3-15y) yes [88] BTW 1 experienced dentist in oral radiology yes [38] PAN radiologists (unspecified) yes [34] (30/11/10) PAN 1 dentist + 1 radiologist yes [40] (447/127/61) PAN -yes [37] (485/69/139) PERI 2 dentists + 1 resident dentist yes [61] (80/-/20) PAN 2 dentists yes [84] (400/50/50) BTW 1 radiologist (11y) and a research assistant (3y) yes [62] (1200/150/150) PAN Clinicians (unspecified) yes [85] (935/117/117) PERI 1 radiologist (12y) and a research assistant (2y) yes [51] (76/-/32) PAN 3 dentists (+3y) yes [52] (980/-/420) PAN 3 dentists (+3y) yes [63] (90/-/10) PAN Radiologists (unspecified, +5y) yes [53] (1104/111/121) RVG Dentists (unspecified) yes [89] (175/-/75) PAN Dentists (unspecified, +5y) yes [87] (457/-/195) Unspecified 4 dentists yes [78] (193/83/1224) PAN -no [66] (2507/835/835) PAN 1 dentist + 1 dental student (last year) yes [79] PANs + 682 PERI/BTW * [3] PAN + PERI + BTW 3 dentists yes [86] PAN dentists (unspecified) yes [35] (1005/335/335) BTW 2 dentists yes [80] (1000/0/200) BTW Dentists (unspecified) no [41] ( (iii) number and expertise of professionals involved in the annotation task and (iv) whether the paper presents any information about the dataset annotation protocol. This information is used to investigate RQ1 Concerning RQ1.1, the selected papers exploited three different types of radiograph: panoramic (forty-six papers, 66.67%), periapical (nine papers, 13.04%), and bitewing (eight papers, 11.59%).…”

Section: Datasetsmentioning

confidence: 99%

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

Carneiro,

Zancan,

Tirapelli

et al. 2023

Preprint

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Purpose: Oral health is an indicator of individuals' health and quality of life. Consequently, it is a concerning topic faced everyday by healthcare professionals. X-ray images are essential resources for detecting oral problems. Artificial Intelligence has gained attention in the detection of oral diseases and conditions. We carry out a systematic mapping in the literature regarding the application of deep learning in dental radiographs for detection, segmentation, and classification of teeth, caries, and restorations. Methods: Through automated searches in the ACM Digital Library, IEEE Xplore Digital Library, PubMed, and Scopus, 394 primary articles were found, published between 2012-2023. After applying inclusion and exclusion criteria, 69 articles were read and analyzed considering: consistency and adequacy of the used databases, implemented techniques, and results. Results: 40.82% of the analyzed articles did not present clear information on the approval of ethics committees to carry out the research. Although the application of computational techniques in oral health is an interdisciplinary topic, 37.68% of the 69 surveys were conducted by teams composed only of professionals from one area. 66.67% of the articles explored only panoramic radiographs and the most used metrics were accuracy, recall, and precision. The U-Net and Mask R-CNN networks were the most applied. Conclusion: Despite the increase in the number of investigations, some challenges, such as the low availability of public data sets, the lack of details in the developed methodologies, and the lack of standardization when presenting results, complicate a fair comparison between papers, which is an obstacle to be overcome.

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Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs

Cited by 47 publications

References 22 publications

A Novel Collaborative Learning Model for Teeth and Fillings in Radiographs

A Novel Collaborative Learning Model for Teeth and Fillings in Radiographs

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

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