Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population

Yeom, Han-Gyeol; Lee, Byung-Do; Lee, Wan; Lee, Taehan; Yun, Jong Pil

doi:10.1038/s41598-023-48960-2

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…AI has primarily been used for automated age estimation by analyzing tooth development stages [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], tooth and bone parameters [ 48 , 49 , 50 ], bone age measurements [ 51 ], and pulp–tooth ratio [ 52 , 53 ]. We gathered data from the studies included, but due to the varied data samples used to assess AI model performance, a meta-analysis could not be conducted.…”

Section: Resultsmentioning

confidence: 99%

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

Khanagar,

Albalawi,

Alshehri

et al. 2024

Diagnostics

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Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp–tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

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Section: Resultsmentioning

confidence: 99%

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

Khanagar,

Albalawi,

Alshehri

et al. 2024

Diagnostics

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Age Group Classification From Dental Panoramic Radiographs Using Deep Learning Techniques

Bussaban,

Boonchieng,

Panyarak

et al. 2024

IEEE Access

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Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms

Shu,

Xie,

Zou

2024

Photonics

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Dorsal hand vein recognition, with unique stable and reliable advantages, has attracted considerable attention from numerous researchers. In this case, the dorsal hand vein images captured by the means of transmission infrared imaging are clearer than those collected by other infrared methods, enabling it to be more suitable for the biometric applications. However, less attention is paid to individual age estimation based on dorsal hand veins. To this end, this paper proposes an efficient dorsal hand vein age estimation model using a deep neural network with attention mechanisms. Specifically, a convolutional neural network (CNN) is developed to extract the expressive features for age estimation. Simultaneously, another deep residual network is leveraged to strengthen the representation ability on subtle dorsal vein textures. Moreover, variable activation functions and pooling layers are integrated into the respective streams to enhance the nonlinearity modeling of the dual-stream model. Finally, a dynamic attention mechanism module is embedded into the dual-stream network to achieve multi-modal collaborative enhancement, guiding the model to concentrate on salient age-specific features. To evaluate the performance of dorsal hand vein age estimation, this work collects dorsal hand vein images using the transmission near-infrared spectrum from 300 individuals across various age groups. The experimental results show that the dual-stream enhanced network with the attention mechanism significantly improves the accuracy of dorsal hand vein age estimation in comparison with other deep learning approaches, indicating the potential of using near-infrared dorsal hand vein imaging and deep learning technology for efficient human age estimation.

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Estimating chronological age through learning local and global features of panoramic radiographs in the Korean population

Cited by 4 publications

References 25 publications

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

Age Group Classification From Dental Panoramic Radiographs Using Deep Learning Techniques

Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms

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