Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

Murata, Makoto; Ariji, Yoshiko; Ohashi, Yasufumi; Kawai, Taisuke; Fukuda, Motoki; Funakoshi, Takuma; Kise, Yoshitaka; Nozawa, Michihito; Katsumata, Akitoshi; Fujita, Hiroshi; Ariji, Eiichiro

doi:10.1007/s11282-018-0363-7

Cited by 154 publications

(139 citation statements)

References 19 publications

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“…A DCNN‐based CAD system showed strong agreement with experienced oral and maxillofacial radiologists in detecting osteoporosis; this system could provide information to dentists for early detection of osteoporosis, allowing asymptomatic patients to be referred to the appropriate medical professionals for preventive care (Lee et al ). A recent study by Murata et al () reported that deep learning systems could diagnose maxillary sinusitis on a panoramic radiograph at a rate comparable to that of radiologists and that their performance was superior to that of dental residents (Murata et al ).…”

Section: Discussionmentioning

confidence: 99%

“…CNNs have been successfully used for automatic assessment of various medical and dental problems, including image‐based automated diagnosis to detect lung and brain lesions (Akkus et al , Song et al , Wang et al , Blanc‐Durand et al ), breast cancer in mammography images (Becker et al ), colorectal polyps and prostate cancer (Wang et al , Byrne et al ), skin cancer (Esteva et al ), diabetic retinopathy in retinal fundus photographs (Gulshan et al ), hip osteoarthritis (Xue et al ) and bone age assessment (Lee et al ). In dentistry, CNNs have been applied to detect carious lesions, periapical lesions, tooth eruption and numbering, vertical root fractures, assess root morphology or periodontal bone loss, dental and jaw pathosis, osteoporosis, and maxillary sinusitis on dental radiographs (Kositbowornchai et al , Miki et al , Ezhov et al , Murata et al , Poedjiastoeti & Suebnukarn , Lee et al ,b, Zakirov et al , Zakirov et al , Chen et al , Ekert et al , Hiraiwa et al , Hwang et al , Krois et al , Tuzoff et al ).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The main advantage of panoramic radiography is the ability to detect tooth-and jaw-related objects simultaneously [27]. Despite the plethora of images available, few studies [19,[28][29][30][31] have applied CNNs to their classifications and diagnoses. Studies that used panoramic radiographs often involved diseases related to the jawbone [28,29,31] and the maxillary sinus [19].…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Networks for Dental Implant System Classification

et al. 2020

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In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

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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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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

Cited by 154 publications

References 19 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

Deep Neural Networks for Dental Implant System Classification

Radiomics and Machine Learning in Oral Healthcare

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