Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network

Lee, Jae‐Hong; Kim, Do-Hyung; Jeong, Seong‐Nyum

doi:10.1111/odi.13223

Cited by 160 publications

(124 citation statements)

References 23 publications

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“…In this sense, it would be interesting to apply neural networks and artificial intelligence in other types of radiological studies such as cone beam computed tomography (CBCT) or cephalometry, which allow clinicians to make a complete anatomical examination. Lee et al evaluated the detection and diagnosis of different lesions employing CBCT and a deep convolutional neural network [35]. Before being possible to detect the variables analyzed in this review, teeth must be detected.…”

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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Section: Discussionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The methodological quality of the included studies was evaluated using the assessment criteria proposed by Hung et al [11]. For proposed AI models for diagnosis/classification of a certain condition, four studies [16][17][18][19] were rated as having a "high" or an "unclear" risk of concern in the domain of subject selection because the testing dataset only consisted of images from subjects with the condition of interest. With regard to the selection of reference standards, all studies were considered as "low" risk of concern as expert judgment and clinical or pathological examination was applied as the reference standard.…”

Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning

confidence: 99%

“…Table 1 exhibits the included studies regarding the use of AI for 3D imaging in DMFR. These studies focused on three main applications, including automated diagnosis of dental and maxillofacial diseases [16][17][18][19][20][28][29][30][31][32], localization of anatomical landmarks for orthodontic and orthognathic treatment planning [21,22,[33][34][35], and improvement of image quality [23,36].…”

Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning

confidence: 99%

“…Manual segmentation of cystic lesions on multiple CBCT slices is time-consuming, which limits the efficiency of the algorithms and also their implementation for routine clinical use. Lee et al proposed deep learning algorithms, respectively, using panoramic radiographs and CBCT images for the detection and diagnosis of periapical cysts, dentigerous cysts, and keratocysts [19]. It was reported that automatic edge detection techniques can segment cystic lesions more efficiently and accurately than manual segmentation.…”

Section: Automated Diagnosis Of Dental and Maxillofacial Diseasesmentioning

confidence: 99%

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Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Hung

Yeung

Tanaka

et al. 2020

IJERPH

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The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

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“…Convolutional neural networks (CNNs) continue to advance and are being applied in a variety of dental and maxillofacial fields. This can perform radiographic detection of periodontal bone loss 4 and be used to diagnose cystic lesions using panoramic and cone beam computed tomographic images 5 . Survival prediction of oral cancer patients are also possible 6 .…”

mentioning

confidence: 99%

Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs

Jeong

Yun

Yeom

et al. 2020

Sci Rep

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Facial photographs of the subjects are often used in the diagnosis process of orthognathic surgery. The aim of this study was to determine whether convolutional neural networks (CNNs) can judge soft tissue profiles requiring orthognathic surgery using facial photographs alone. 822 subjects with dentofacial dysmorphosis and / or malocclusion were included. Facial photographs of front and right side were taken from all patients. Subjects who did not need orthognathic surgery were classified as Group I (411 subjects). Group II (411 subjects) was set up for cases requiring surgery. CNNs of VGG19 was used for machine learning. 366 of the total 410 data were correctly classified, yielding 89.3% accuracy. The values of accuracy, precision, recall, and F1 scores were 0.893, 0.912, 0.867, and 0.889, respectively. As a result of this study, it was found that CNNs can judge soft tissue profiles requiring orthognathic surgery relatively accurately with the photographs alone.

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Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network

Cited by 160 publications

References 23 publications

Dental Images Recognition Technology and Applications: A Literature Review

Dental Images Recognition Technology and Applications: A Literature Review

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs

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