Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence

Ariji, Yoshiko; Fukuda, Motoki; Kise, Yoshitaka; Nozawa, Michihito; Yanashita, Yudai; Fujita, Hiroshi; Katsumata, Akitoshi; Ariji, Eiichiro

doi:10.1016/j.oooo.2018.10.002

Cited by 129 publications

(94 citation statements)

References 18 publications

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“…Various studies have also evaluated the performance of deep learning image classification for the diagnosis of lymph node metastases. The performance of these AI systems was not significantly different from that of radiologists, suggesting that these systems could be a useful method for diagnostic support (Ariji et al ). Deep learning systems are also capable of detecting the impact of systemic diseases on oral tissues.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

et al. 2020

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Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, € Ozy€ urek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. ) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis.Results The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05).Conclusions Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application.

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Section: Discussionmentioning

confidence: 99%

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

et al. 2020

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“…When combining the term “artificial intelligence” and “radiology” and “dental” or “oral,” 196 articles were retrieved in Pubmed database. Some recent studies have demonstrated that CNN‐based methods may be used in dental images for several purposes, as demonstrated in Table …”

Section: Ai Revolutionizing Oral Health Carementioning

confidence: 99%

Radiomics and Machine Learning in Oral Healthcare

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Willems

et al. 2020

Proteomics Clinical Apps

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The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

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“…Screening for OSCC can involve cytology (9), vital staining (10,11), and fluorescence visualization (FV) (12). FV is non-invasive, convenient, and real-time, and the examinations can be repeated using optical instruments (OI) (12,13). More specifically FV uses blue light (400-460 nm) to visualize collagen cross-links (CCL) or flavin adenine dinucleotide (FAD) and nicotinamide adenine dinucleotide (NADH) (13).…”

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confidence: 99%

“…FV is non-invasive, convenient, and real-time, and the examinations can be repeated using optical instruments (OI) (12,13). More specifically FV uses blue light (400-460 nm) to visualize collagen cross-links (CCL) or flavin adenine dinucleotide (FAD) and nicotinamide adenine dinucleotide (NADH) (13). A selective filter allows the viewer to directly visualize the apple-green autofluorescence emitted from normal tissue (FV retention, FVR).…”

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The Utility of Optical Instrument “ORALOOK®” in the Early Detection of High-risk Oral Mucosal Lesions

2019

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Background/Aim: Oral cancer screening is important for early detection and early treatment, which help improve survival rates. Biopsy is invasive and painful, while fluorescence visualization is non-invasive, convenient, and realtime, and examinations can be repeated using optical instruments. The purpose of this study was to clarify the usefulness of an optical instrument in oral screening. Patients and Methods: A total of 201 patients, who were examined using an optical instrument in our Department between 2017 and 2018, were enrolled in this study. Fluorescence visualization images were analyzed using subjective and objective evaluations. Results: Subjective evaluations for detecting oral cancer and oral epithelial dysplasia offered 83.3% sensitivity and 75.7% specificity. Regarding the objective evaluations for detecting oral cancer and oral epithelial dysplasia, sensitivity and specificity were 47.4% and 72.4% for luminance value, 94.7% and 79.6% for luminance ratio, and 100.0% and 68.0% coefficient of variation. Conclusion: Fluorescence visualization using optical instruments is useful for oral cancer screening.

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Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence

Cited by 129 publications

References 18 publications

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

Radiomics and Machine Learning in Oral Healthcare

The Utility of Optical Instrument “ORALOOK®” in the Early Detection of High-risk Oral Mucosal Lesions

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