Automated chart filing on panoramic radiographs using deep learning

Vinayahalingam, Shankeeth; Goey, Ru-shan; Kempers, Steven; Schoep, Julian; Cherici, Teo; Moin, David Anssari; Hanisch, Marcel

doi:10.1016/j.jdent.2021.103864

Cited by 33 publications

(43 citation statements)

References 17 publications

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“…Although resolution is not as detailed as intra-oral radiographs for examination of the teeth, many changes in calcification of the dental structures and in ossification of the surrounding bone can aid in the identification of dental diseases, such as caries (decay), periodontal bone loss, and bone lesions [ 1 ]. As far as cone-beam computed tomography (CBCT) systems are developed and becoming more and more popular for imaging comprehensive 3D volumetric information concerning oral soft tissues, bones, and teeth, PRs remain a very common initial X-ray and screening tool in the diagnostic process in dentistry [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. However, although CBCT provides more data, the analysis is laborious and time-consuming [ 3 , 7 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Application in Assessment of Panoramic Radiographs

et al. 2022

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The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Application in Assessment of Panoramic Radiographs

et al. 2022

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“…Different types of images were used by different researchers based on the techniques they used in DI. Radiographic images [ 16 , 41 , 43 , 56 , 63 , 64 , 65 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 87 , 157 ], near-infrared light transillumination (NILT) [ 88 , 89 , 90 ], intraoral images [ 66 , 86 , 91 , 92 , 93 , 95 , 96 , 97 , 158 , 159 , 160 ], 3D model [ 102 , 113 , 114 , 115 , 161 ] were used in the research for dental diseases diagnostic on the 3D dental model. The studies on the dental disease’s diagnostic on the CBCT dental model used the CT images [ 124 ], 3D CT scans […”

Section: Resultsmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

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“…In their study, the F1 score was found to be 0.91 for the crown and 0.82 for the pontic. Similarly, in the study by Vinayahalingam et al [20], 2000 images were randomly collected, and then blurred and incomplete PRs were excluded. Teir F1 score was 0.951.…”

Section: Discussionmentioning

confidence: 99%

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Altan

Güneç

ÇINAR

et al. 2022

Scientific Programming

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Aim. This study applied a CNN (convolutional neural network) algorithm to detect prosthetic restorations on panoramic radiographs and to automatically detect these restorations using deep learning systems. Materials and Methods. This study collected a total of 5126 panoramic radiographs of adult patients. During model training, .bmp, .jpeg, and .png files for images and .txt files containing five different types of information are required for the labels. Herein, 10% of panoramic radiographs were used as a test dataset. Owing to labeling, 2988 crowns and 2969 bridges were formed in the dataset. Results. The mAP and mAR values were obtained when the confidence threshold was set at 0.1. TP, FP, FN, precision, recall, and F1 score values were obtained when the confidence threshold was 0.25. The YOLOv4 model demonstrated that accurate results could be obtained quickly. Bridge results were found to be more successful than crown results. Conclusion. The detection of prosthetic restorations with artificial intelligence on panoramic radiography, which is widely preferred in clinical applications, provides convenience to physicians in terms of diagnosis and time management.

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Automated chart filing on panoramic radiographs using deep learning

Cited by 33 publications

References 17 publications

Artificial Intelligence Application in Assessment of Panoramic Radiographs

Artificial Intelligence Application in Assessment of Panoramic Radiographs

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

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