Automated estimation of chronological age from panoramic dental X-ray images using deep learning

Milošević, Denis; Vodanović, Marin; Galić, Ivan; Subašić, Marko

doi:10.1016/j.eswa.2021.116038

Cited by 54 publications

(54 citation statements)

References 43 publications

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“…It should be noted that this team could not know the precise metrical age. Very high accuracy of the produced models was presented in their works by Milošević et al [49] and Kahaki et al [50]. However, despite the high values of the indicators defining the networks, the error was measured in years rather than individual months.…”

Section: Discussionmentioning

confidence: 91%

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Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Zaborowicz

Biedziak

et al. 2022

Sensors

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Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.

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Section: Discussionmentioning

confidence: 91%

“…Deep convolutional neural networks were also presented in the works of Milošević et al [49] and Kahaki et al [50]. They evaluated the accuracy of dental age estimation from X-rays.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Zaborowicz

Biedziak

et al. 2022

Sensors

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“…dikategorikan. Metrik kinerja dan deskripsi kumpulan data dianalisis sampai batas tertentu [27]. Penelitian ini mengembangkan metode Ekstraksi ciri dan algoritma untuk mendapatkan luas area berlubang pada gigi untuk mengidentifikasi kelainan pada gigi untuk selanjutnya agar dapat dilakukan tindakan medis.…”

Section: Pendahuluanunclassified

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

Sumijan

2022

komtekinfo

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Many of us as Muslims do not know how to act when the call to prayer is heard, which turns out to have a very noble virtue. This animation is expected to provide insight for all of us about the virtues of answering the call to prayer. In addition, this animation can also attract and entertain the audience. Technological advances that are growing rapidly today cover all fields. One of them is the field of multimedia such as animation. This study aims to create 3D animation that is used as a learning medium. The application has the theme of etiquette when calling the call to prayer. 3D animation is the process of placing objects and characters in three-dimensional space and manipulating them to create the illusion of motion. Design in application development using Blender software and applications in making animation. The results of the resulting application present an interactive form of teaching for users in providing knowledge about manners when the call to prayer is heard. With this application, it is hoped that it can provide an alternative to provide a form of teaching, especially for children to explain etiquette when calling the call to prayer

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“…It becomes difficult for dentists to find carious regions in the dental images due to the lack of usable carious region detection and localization tools. The deep learning approaches that extract high-level image features and localize the dental image carious regions are discussed in the literature, primarily focusing X-ray based radiographic images ( Milošević et al, 2022 ; Morid, Borjali & Fiol, 2021 ). Moreover, a functional tool to detect and localize dental carious regions is unavailable for mixed images, i.e ., colored photographs and X-ray radiographs.…”

Section: Introductionmentioning

confidence: 99%

A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

Rashid

Javid

Khan

et al. 2022

PeerJ Computer Science

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Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions’ localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.

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Automated estimation of chronological age from panoramic dental X-ray images using deep learning

Cited by 54 publications

References 43 publications

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Pengembangan Ekstraksi dalam Identifikasi Kelainan Gigi pada Citra Dental X-ray imaging (DXRI)

A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

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