Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Ariji, Yoshiko; Mori, M; Fukuda, Motoki; Katsumata, Akitoshi; Ariji, Eiichiro

doi:10.1016/j.oooo.2022.05.014

Cited by 17 publications

(14 citation statements)

References 29 publications

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“… Yoo et al (2021) determined the sensitivity and specificity using positive predictive value (PPV) calculations. Furthermore, the studies reviewed in this work did not use a consistent mathematical formula ( Ariji et al, 2022 , Buyuk et al, 2022 , Celik, 2022 , Choi et al, 2022 , Maruta et al, 2023 , Takebe et al, 2022 , Vinayahalingam et al, 2019 , Yoo et al, 2021 , Zhu et al, 2021 ). Celik, 2022 , Choi et al, 2022 , Takebe et al, 2022 , and Zhu et al (2021) , used PPV calculations to determine precision in their research.…”

Section: Discussionmentioning

confidence: 99%

“…GoogLeNet, VGG-16, AlexNet ( Buyuk et al, 2022 , Fukuda et al, 2020 ), YOLOv3 ( Takebe et al, 2022 ), YOLOv4 ( Zhu et al, 2021 ), ResNet-50 ( Choi et al, 2022 , Sukegawa et al, 2022b ) are some deep learning architectures that have been investigated for radiographic evaluation of impacted third molar related to the mandibular canal. In addition, U-Net is also used for image segmentation of the mandibular canal ( Ariji et al, 2022 , Buyuk et al, 2022 , Vinayahalingam et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

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The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Faadiya,

Widyaningrum,

Arindra

et al. 2024

The Saudi Dental Journal

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Faadiya,

Widyaningrum,

Arindra

et al. 2024

The Saudi Dental Journal

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“…In recent years, research on diagnostic imaging using deep learning systems has been rapidly progressing, and its usefulness in the maxillofacial region has been reported using panoramic radiographs. 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 A deep learning system is an artificial intelligence machine learning method that allows a computer to learn tasks in the same way as humans, based on a neural network system that imitates the neurons in the human brain. Many authors have reported the clinical usefulness of deep learning systems for panoramic diagnosis, and several studies have addressed the classification of cyst-like lesions including NDCs and radicular cysts.…”

Section: Introductionmentioning

confidence: 99%

Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs

Kise,

Kuwada,

Mori

et al. 2024

Imaging Sci Dent

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Purpose The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

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“…These studies have been focused on various aspects, including ambiguity classification, visualization of impacted third molars, and panoptic segmentation of the mandibular canal along with other structures using deep learning. 9 10 11 …”

Section: Introductionmentioning

confidence: 99%

“…However, these investigations have not considered the mental foramen region during training and/or have not employed datasets representing over 2,000 participants. 9 10 11 The mental foramen is a critical anatomical landmark that must be accurately identified and preserved to avoid complications. 12 Consequently, incorporating the mental foramen into mandibular canal segmentation tasks could greatly contribute to diagnosis and analysis.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study

Aung,

Lim,

Han

et al. 2024

Imaging Sci Dent

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Purpose The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

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Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Cited by 17 publications

References 29 publications

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study

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