Low reproducibility between oral radiologists and general dentists with regards to radiographic diagnosis of caries

Esmaeili, Elmira Pakbaznejad; Pakkala, Tuomas; Haukka, Jari; Siukosaari, Päivi

doi:10.1080/00016357.2018.1460490

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…As a retrospective study, it was not possible to obtain a gold-standard for the conditions assessed. However, previous studies comparing observers' experience showed good reproducibility for specialists (1) or professionals with more than five years dealing with imaging diagnosis (2). An important point to emphasize is that the image alone is not responsible for the correct diagnosis.…”

Section: Discussionmentioning

confidence: 95%

“…An important point to emphasize is that the image alone is not responsible for the correct diagnosis. It is dependent on the observer's experience (1,2) and the evaluation method used (5), among other factors that might have influence, such as the subjectivity of this task. It is noticeable the change in diagnostic thinking according to the evaluation method used in the present study.…”

Section: Discussionmentioning

confidence: 99%

“…The goal of health care is to provide diagnostic procedures, preventive care, and restorative care (1). The primary method for diagnosis is the clinical examination, which might use a supplemental diagnostic tool like the radiographic examination.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Different Evaluation Methods for Diagnostic Tasks in Panoramic Radiography

Brasil

Gaêta‐Araujo

Almeida

et al. 2021

Odovtos - Int J Dent Sc

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The aim of this study was to evaluate the observers’ diagnostic performance in panoramic radiography using monitor, tablet, X-ray image view box, and against window daylight as a visualization method in different diagnostic tasks. Thirty panoramic radiography were assessed by three calibrated observers for each visualization method, in standardized light conditions, concerning dental caries, widened periodontal ligament space, and periapical bone defects from the four first molars; mucosal thickening and retention cysts in maxillary sinus; and stylo-hyoid ligament calcification and atheroma. A five-point confidence scale was used. The standard-reference was performed by two experienced observers. Diagnostic values using window light were significantly lower for caries and periapical bone defect and retention cyst, stylo-hyoid ligament calcification detection (p<0.05). For atheroma detection, X-ray image view box, tablet, and widow light had lower accuracy than the evaluation on the monitor (p<0.05). Observer’s diagnostic performances are worsened using window light as an evaluation method for panoramic radiography for dental, sinus, and calcification disorders, while the monitor was the most reliable method.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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Impact of Different Evaluation Methods for Diagnostic Tasks in Panoramic Radiography

Brasil

Gaêta‐Araujo

Almeida

et al. 2021

Odovtos - Int J Dent Sc

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“…Clinical examination is the main method for caries detection, with radiographic examination being a complementary diagnostic tool [40]. According to experience and scientific literature, intraoral bitewing images are the most effective in detecting caries lesions [41]. However, only one study included in this review employed bitewings to detect caries.…”

Section: Discussionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

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“…The neural network achieved an accuracy rate of 85%. Neural networks may be valuable for evaluating radiographic bone loss and generating image-based periodontal diagnostics [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images

Almalki

Din

Ramzan

et al. 2022

Sensors

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The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

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Low reproducibility between oral radiologists and general dentists with regards to radiographic diagnosis of caries

Abstract: The reproducibility between specialists in oral radiology and general dentists for detecting caries in DPTs was low.

Cited by 7 publications

References 14 publications

Impact of Different Evaluation Methods for Diagnostic Tasks in Panoramic Radiography

Impact of Different Evaluation Methods for Diagnostic Tasks in Panoramic Radiography

Dental Images Recognition Technology and Applications: A Literature Review

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images

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